diff --git a/.github/workflows/github-deploy.yml b/.github/workflows/github-deploy.yml
index 059d01612..81ef7e602 100644
--- a/.github/workflows/github-deploy.yml
+++ b/.github/workflows/github-deploy.yml
@@ -5,13 +5,18 @@ on:
- gh-page
jobs:
build-and-deploy:
- runs-on: ubuntu-latest
+ runs-on: ubuntu-22.04
steps:
- name: Checkout 🛎️
uses: actions/checkout@v2
with:
persist-credentials: false
+ - name: Set up Ruby 2.7.8 💎
+ uses: ruby/setup-ruby@v1
+ with:
+ ruby-version: 2.7.8
+
- name: Install and Build 🔧
run: |
make install-deb
@@ -22,6 +27,4 @@ jobs:
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: _site
- allow_empty_commit: true
- enable_jekyll: true
- publish_branch: master # deploying branch
\ No newline at end of file
+ publish_branch: master # deploying branch
diff --git a/.gitignore b/.gitignore
index ac9f60c73..531368c30 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,7 +16,7 @@ _development_tool/*
*.scssc
# Theme Gem files
-Gemfile.lock
+# Gemfile.lock
*.gem
.gems
jekyll-theme-type-on-strap.gemspec
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 02fa2f9c0..000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,31 +0,0 @@
-language: ruby
-rvm:
- - 2.3.3
-before_install:
- - gem update --system
- - gem install bundler
-script:
- - bundle install
- - bundle exec jekyll build
-notifications:
- email: false
-after_script:
- - gem install type-on-strap
- - gem install jekyll-theme-type-on-strap
-branches:
- only:
- - gh-pages
- - master
- - /^v.*$/
-env:
- global:
- - NOKOGIRI_USE_SYSTEM_LIBRARIES=true
-sudo: false
-deploy:
- provider: rubygems
- api_key:
- secure: 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
- gem: type-on-strap
- on:
- tags: true
- repo: sylhare/Type-on-Strap
diff --git a/Gemfile b/Gemfile
index 3be9c3cd8..8a608015d 100644
--- a/Gemfile
+++ b/Gemfile
@@ -1,2 +1,7 @@
source "https://rubygems.org"
+ruby '2.7.8'
gemspec
+group :jekyll_plugins do
+ gem 'jekyll-scholar'
+ gem 'github-pages'
+end
diff --git a/Gemfile.lock b/Gemfile.lock
new file mode 100644
index 000000000..9bbae37d0
--- /dev/null
+++ b/Gemfile.lock
@@ -0,0 +1,295 @@
+PATH
+ remote: .
+ specs:
+ type-on-strap (1.3.0)
+ jekyll (~> 3.8, >= 3.8.5)
+ jekyll-paginate (~> 1.1)
+ jekyll-seo-tag (~> 2.6)
+
+GEM
+ remote: https://rubygems.org/
+ specs:
+ activesupport (6.0.6)
+ concurrent-ruby (~> 1.0, >= 1.0.2)
+ i18n (>= 0.7, < 2)
+ minitest (~> 5.1)
+ tzinfo (~> 1.1)
+ zeitwerk (~> 2.2, >= 2.2.2)
+ addressable (2.8.1)
+ public_suffix (>= 2.0.2, < 6.0)
+ bibtex-ruby (4.4.7)
+ latex-decode (~> 0.0)
+ citeproc (1.0.10)
+ namae (~> 1.0)
+ citeproc-ruby (1.1.14)
+ citeproc (~> 1.0, >= 1.0.9)
+ csl (~> 1.6)
+ coffee-script (2.4.1)
+ coffee-script-source
+ execjs
+ coffee-script-source (1.11.1)
+ colorator (1.1.0)
+ commonmarker (0.23.6)
+ concurrent-ruby (1.1.10)
+ csl (1.6.0)
+ namae (~> 1.0)
+ rexml
+ csl-styles (1.0.1.11)
+ csl (~> 1.0)
+ dnsruby (1.61.9)
+ simpleidn (~> 0.1)
+ em-websocket (0.5.3)
+ eventmachine (>= 0.12.9)
+ http_parser.rb (~> 0)
+ ethon (0.16.0)
+ ffi (>= 1.15.0)
+ eventmachine (1.2.7)
+ execjs (2.8.1)
+ faraday (2.7.2)
+ faraday-net_http (>= 2.0, < 3.1)
+ ruby2_keywords (>= 0.0.4)
+ faraday-net_http (3.0.2)
+ ffi (1.15.5)
+ forwardable-extended (2.6.0)
+ gemoji (3.0.1)
+ github-pages (227)
+ github-pages-health-check (= 1.17.9)
+ jekyll (= 3.9.2)
+ jekyll-avatar (= 0.7.0)
+ jekyll-coffeescript (= 1.1.1)
+ jekyll-commonmark-ghpages (= 0.2.0)
+ jekyll-default-layout (= 0.1.4)
+ jekyll-feed (= 0.15.1)
+ jekyll-gist (= 1.5.0)
+ jekyll-github-metadata (= 2.13.0)
+ jekyll-include-cache (= 0.2.1)
+ jekyll-mentions (= 1.6.0)
+ jekyll-optional-front-matter (= 0.3.2)
+ jekyll-paginate (= 1.1.0)
+ jekyll-readme-index (= 0.3.0)
+ jekyll-redirect-from (= 0.16.0)
+ jekyll-relative-links (= 0.6.1)
+ jekyll-remote-theme (= 0.4.3)
+ jekyll-sass-converter (= 1.5.2)
+ jekyll-seo-tag (= 2.8.0)
+ jekyll-sitemap (= 1.4.0)
+ jekyll-swiss (= 1.0.0)
+ jekyll-theme-architect (= 0.2.0)
+ jekyll-theme-cayman (= 0.2.0)
+ jekyll-theme-dinky (= 0.2.0)
+ jekyll-theme-hacker (= 0.2.0)
+ jekyll-theme-leap-day (= 0.2.0)
+ jekyll-theme-merlot (= 0.2.0)
+ jekyll-theme-midnight (= 0.2.0)
+ jekyll-theme-minimal (= 0.2.0)
+ jekyll-theme-modernist (= 0.2.0)
+ jekyll-theme-primer (= 0.6.0)
+ jekyll-theme-slate (= 0.2.0)
+ jekyll-theme-tactile (= 0.2.0)
+ jekyll-theme-time-machine (= 0.2.0)
+ jekyll-titles-from-headings (= 0.5.3)
+ jemoji (= 0.12.0)
+ kramdown (= 2.3.2)
+ kramdown-parser-gfm (= 1.1.0)
+ liquid (= 4.0.3)
+ mercenary (~> 0.3)
+ minima (= 2.5.1)
+ nokogiri (>= 1.13.6, < 2.0)
+ rouge (= 3.26.0)
+ terminal-table (~> 1.4)
+ github-pages-health-check (1.17.9)
+ addressable (~> 2.3)
+ dnsruby (~> 1.60)
+ octokit (~> 4.0)
+ public_suffix (>= 3.0, < 5.0)
+ typhoeus (~> 1.3)
+ html-pipeline (2.14.3)
+ activesupport (>= 2)
+ nokogiri (>= 1.4)
+ http_parser.rb (0.8.0)
+ i18n (0.9.5)
+ concurrent-ruby (~> 1.0)
+ jekyll (3.9.2)
+ addressable (~> 2.4)
+ colorator (~> 1.0)
+ em-websocket (~> 0.5)
+ i18n (~> 0.7)
+ jekyll-sass-converter (~> 1.0)
+ jekyll-watch (~> 2.0)
+ kramdown (>= 1.17, < 3)
+ liquid (~> 4.0)
+ mercenary (~> 0.3.3)
+ pathutil (~> 0.9)
+ rouge (>= 1.7, < 4)
+ safe_yaml (~> 1.0)
+ jekyll-avatar (0.7.0)
+ jekyll (>= 3.0, < 5.0)
+ jekyll-coffeescript (1.1.1)
+ coffee-script (~> 2.2)
+ coffee-script-source (~> 1.11.1)
+ jekyll-commonmark (1.4.0)
+ commonmarker (~> 0.22)
+ jekyll-commonmark-ghpages (0.2.0)
+ commonmarker (~> 0.23.4)
+ jekyll (~> 3.9.0)
+ jekyll-commonmark (~> 1.4.0)
+ rouge (>= 2.0, < 4.0)
+ jekyll-default-layout (0.1.4)
+ jekyll (~> 3.0)
+ jekyll-feed (0.15.1)
+ jekyll (>= 3.7, < 5.0)
+ jekyll-gist (1.5.0)
+ octokit (~> 4.2)
+ jekyll-github-metadata (2.13.0)
+ jekyll (>= 3.4, < 5.0)
+ octokit (~> 4.0, != 4.4.0)
+ jekyll-include-cache (0.2.1)
+ jekyll (>= 3.7, < 5.0)
+ jekyll-mentions (1.6.0)
+ html-pipeline (~> 2.3)
+ jekyll (>= 3.7, < 5.0)
+ jekyll-optional-front-matter (0.3.2)
+ jekyll (>= 3.0, < 5.0)
+ jekyll-paginate (1.1.0)
+ jekyll-readme-index (0.3.0)
+ jekyll (>= 3.0, < 5.0)
+ jekyll-redirect-from (0.16.0)
+ jekyll (>= 3.3, < 5.0)
+ jekyll-relative-links (0.6.1)
+ jekyll (>= 3.3, < 5.0)
+ jekyll-remote-theme (0.4.3)
+ addressable (~> 2.0)
+ jekyll (>= 3.5, < 5.0)
+ jekyll-sass-converter (>= 1.0, <= 3.0.0, != 2.0.0)
+ rubyzip (>= 1.3.0, < 3.0)
+ jekyll-sass-converter (1.5.2)
+ sass (~> 3.4)
+ jekyll-scholar (5.16.0)
+ bibtex-ruby (~> 4.0, >= 4.0.13)
+ citeproc-ruby (~> 1.0)
+ csl-styles (~> 1.0)
+ jekyll (~> 3.0)
+ jekyll-seo-tag (2.8.0)
+ jekyll (>= 3.8, < 5.0)
+ jekyll-sitemap (1.4.0)
+ jekyll (>= 3.7, < 5.0)
+ jekyll-swiss (1.0.0)
+ jekyll-theme-architect (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-cayman (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-dinky (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-hacker (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-leap-day (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-merlot (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-midnight (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-minimal (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-modernist (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-primer (0.6.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-github-metadata (~> 2.9)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-slate (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-tactile (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-theme-time-machine (0.2.0)
+ jekyll (> 3.5, < 5.0)
+ jekyll-seo-tag (~> 2.0)
+ jekyll-titles-from-headings (0.5.3)
+ jekyll (>= 3.3, < 5.0)
+ jekyll-watch (2.2.1)
+ listen (~> 3.0)
+ jemoji (0.12.0)
+ gemoji (~> 3.0)
+ html-pipeline (~> 2.2)
+ jekyll (>= 3.0, < 5.0)
+ kramdown (2.3.2)
+ rexml
+ kramdown-parser-gfm (1.1.0)
+ kramdown (~> 2.0)
+ latex-decode (0.4.0)
+ liquid (4.0.3)
+ listen (3.8.0)
+ rb-fsevent (~> 0.10, >= 0.10.3)
+ rb-inotify (~> 0.9, >= 0.9.10)
+ mercenary (0.3.6)
+ mini_portile2 (2.8.1)
+ minima (2.5.1)
+ jekyll (>= 3.5, < 5.0)
+ jekyll-feed (~> 0.9)
+ jekyll-seo-tag (~> 2.1)
+ minitest (5.17.0)
+ namae (1.1.1)
+ nokogiri (1.13.10)
+ mini_portile2 (~> 2.8.0)
+ racc (~> 1.4)
+ octokit (4.25.1)
+ faraday (>= 1, < 3)
+ sawyer (~> 0.9)
+ pathutil (0.16.2)
+ forwardable-extended (~> 2.6)
+ public_suffix (4.0.7)
+ racc (1.6.2)
+ rake (13.0.6)
+ rb-fsevent (0.11.2)
+ rb-inotify (0.10.1)
+ ffi (~> 1.0)
+ rexml (3.2.5)
+ rouge (3.26.0)
+ ruby2_keywords (0.0.5)
+ rubyzip (2.3.2)
+ safe_yaml (1.0.5)
+ sass (3.7.4)
+ sass-listen (~> 4.0.0)
+ sass-listen (4.0.0)
+ rb-fsevent (~> 0.9, >= 0.9.4)
+ rb-inotify (~> 0.9, >= 0.9.7)
+ sawyer (0.9.2)
+ addressable (>= 2.3.5)
+ faraday (>= 0.17.3, < 3)
+ simpleidn (0.2.1)
+ unf (~> 0.1.4)
+ terminal-table (1.8.0)
+ unicode-display_width (~> 1.1, >= 1.1.1)
+ thread_safe (0.3.6)
+ typhoeus (1.4.0)
+ ethon (>= 0.9.0)
+ tzinfo (1.2.10)
+ thread_safe (~> 0.1)
+ unf (0.1.4)
+ unf_ext
+ unf_ext (0.0.8.2)
+ unicode-display_width (1.8.0)
+ zeitwerk (2.6.6)
+
+PLATFORMS
+ ruby
+
+DEPENDENCIES
+ bundler (~> 2.0, >= 2.0.1)
+ github-pages
+ jekyll-scholar
+ rake (>= 12.3.3)
+ type-on-strap!
+
+BUNDLED WITH
+ 2.1.4
diff --git a/Makefile b/Makefile
index e34573059..96220e7b3 100644
--- a/Makefile
+++ b/Makefile
@@ -4,12 +4,23 @@ clear:
rm -r .sass-cache
install-deb:
- sudo apt-get update && sudo apt-get install ruby-full build-essential zlib1g-dev rsync && \
+ sudo apt-get update && sudo apt-get install -y build-essential zlib1g-dev rsync && \
echo '# Install Ruby Gems to ~/gems' >> ~/.bashrc && \
echo 'export GEM_HOME="$$HOME/gems"' >> ~/.bashrc && \
echo 'export PATH="$$HOME/gems/bin:$$PATH"' >> ~/.bashrc && \
- sudo gem install jekyll bundler
- bundle install
+ ruby -v
+ gem -v
+ sudo gem install ffi -v 1.16.0
+ sudo gem install jekyll -v 4.2.2
+ sudo gem install bundler -v 2.4.22
+ ruby -v
+ gem -v
+ sudo gem install redcarpet
+ ruby -v
+ gem -v
+ bundle install
+ ruby -v
+ gem -v
build:
bundle exec jekyll build
diff --git a/_bibliography/mediaevalbib.bib b/_bibliography/mediaevalbib.bib
index 6796f0338..27fe92423 100644
--- a/_bibliography/mediaevalbib.bib
+++ b/_bibliography/mediaevalbib.bib
@@ -1,1816 +1,5157 @@
-@article{mediaeval00054,
- author = {J Choi and A Janin and G Friedland},
- title = {The 2010 ICSI video location estimation system},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+@inproceedings{formal2020learning,
+ title={Learning to Rank Images with Cross-Modal Graph Convolutions},
+ author={Formal, Thibault and Clinchant, St{\'e}phane and Renders, Jean-Michel and Lee, Sooyeol and Cho, Geun Hee},
+ booktitle={Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14--17, 2020, Proceedings, Part I 42},
+ pages={589--604},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00055,
- author = {O Van Laere and S Schockaert and B Dhoedt},
- title = {Ghent university at the 2011 Placing Task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@inproceedings{chen2022cross,
+ title={Cross-modal ambiguity learning for multimodal fake news detection},
+ author={Chen, Yixuan and Li, Dongsheng and Zhang, Peng and Sui, Jie and Lv, Qin and Tun, Lu and Shang, Li},
+ booktitle={Proceedings of the ACM Web Conference 2022},
+ pages={2897--2905},
+ year={2022}
}
-@article{mediaeval00056,
- author = {M Brenner and E Izquierdo},
- title = {MediaEval Benchmark: Social Event Detection in collaborative photo collections.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{zeng2022keyword,
+ title={Keyword-based diverse image retrieval with variational multiple instance graph},
+ author={Zeng, Yawen and Wang, Yiru and Liao, Dongliang and Li, Gongfu and Huang, Weijie and Xu, Jin and Cao, Da and Man, Hong},
+ journal={IEEE Transactions on Neural Networks and Learning Systems},
+ year={2022},
+ publisher={IEEE}
}
-@article{mediaeval00057,
- author = {X Liu and B Huet and R Troncy},
- title = {EURECOM@ MediaEval 2011 Social Event Detection Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@inproceedings{singhal2022leveraging,
+ title={Leveraging intra and inter modality relationship for multimodal fake news detection},
+ author={Singhal, Shivangi and Pandey, Tanisha and Mrig, Saksham and Shah, Rajiv Ratn and Kumaraguru, Ponnurangam},
+ booktitle={Companion Proceedings of the Web Conference 2022},
+ pages={726--734},
+ year={2022}
}
-@article{mediaeval00058,
- author = {LT Li and J Almeida and R da Silva Torres},
- title = {RECOD Working Notes for Placing Task MediaEval 2011.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{fu2022multi,
+ title={Multi-modal affine fusion network for social media rumor detection},
+ author={Fu, Boyang and Sui, Jie},
+ journal={PeerJ Computer Science},
+ volume={8},
+ pages={e928},
+ year={2022},
+ publisher={PeerJ Inc.}
}
-@article{mediaeval00005,
- author = {J Cigarran and V Fresno and A García-Serrano and D Hernández-Aranda & R Granados},
- title = {UNED at MediaEval 2011: can delicious help us to improve automatic video tagging?},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{guo2022detecting,
+ title={Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF},
+ author={Guo, Haoming and Huang, Tianyi and Huang, Huixuan and Fan, Mingyue and Friedland, Gerald},
+ journal={arXiv preprint arXiv:2205.00377},
+ year={2022}
}
-@article{mediaeval00059,
- author = {Y Wang and L Xie and H Sundaram},
- title = {Social Event Detection with clustering and filtering.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{figueredo2022unsupervised,
+ title={Unsupervised query-adaptive implicit subtopic discovery for diverse image retrieval based on intrinsic cluster quality},
+ author={Figuer{\^e}do, Jos{\'e} Solenir Lima and Calumby, Rodrigo Tripodi},
+ journal={Multimedia Tools and Applications},
+ volume={81},
+ number={30},
+ pages={42991--43011},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00060,
- author = {C Hauff and GJ Houben},
- title = {WISTUD at MediaEval 2011: Placing Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{singh2022semi,
+ title={SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection},
+ author={Singh, Prabhav and Srivastava, Ridam and Rana, KPS and Kumar, Vineet},
+ journal={arXiv preprint arXiv:2205.08159},
+ year={2022}
}
-@article{mediaeval00061,
- author = {S Papadopoulos and C Zigkolis and Y Kompatsiaris and A Vakali},
- title = {CERTH@ MediaEval 2011 Social Event Detection Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{khouakhi2022need,
+ title={The need for training and benchmark datasets for convolutional neural networks in flood applications},
+ author={Khouakhi, Abdou and Zawadzka, Joanna and Truckell, Ian},
+ journal={Hydrology Research},
+ volume={53},
+ number={6},
+ pages={795--806},
+ year={2022},
+ publisher={IWA Publishing}
}
-@article{mediaeval00062,
- author = {I Söke and J Tejedor and M Fapso and J Colás},
- title = {BUT-HCTLab approaches for Spoken Web Search-MediaEval 2011.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{khouakhi2022need,
+ title={The need for training and benchmark datasets for convolutional neural networks in flood applications},
+ author={Khouakhi, Abdou and Zawadzka, Joanna and Truckell, Ian},
+ journal={Hydrology Research},
+ volume={53},
+ number={6},
+ pages={795--806},
+ year={2022},
+ publisher={IWA Publishing}
}
-@article{mediaeval00063,
- author = {G Gninkoun and M Soleymani},
- title = {Automatic violence scenes detection: A multi-modal approach},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@phdthesis{alfarano2022detecting,
+ title={Detecting fake news using natural language processing},
+ author={Alfarano, Giulio},
+ year={2022},
+ school={Politecnico di Torino}
}
-@article{mediaeval00064,
- author = {T Hintsa and S Vainikainen and M Melin},
- title = {Leveraging linked data in Social Event Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{song2022innovation,
+ title={Innovation and Practice of Music Education Path in Colleges and Universities Under the Popularization of 5g Network},
+ author={Song, Xi},
+ year={2022}
}
-@article{mediaeval00065,
- author = {X Anguera},
- title = {Telefonica System for the Spoken Web Search Task at Mediaeval 2011.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011}
+
+@article{lu2022assessing,
+ title={Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography},
+ author={Lu, Yiqian and Yu, Zengjie and Liu, Jikui and An, Qi and Chen, Cong and Li, Ye and Wang, Yishan},
+ journal={Physiological Measurement},
+ volume={43},
+ number={7},
+ pages={075005},
+ year={2022},
+ publisher={IOP Publishing}
}
-@article{mediaeval00066,
- author = {E Barnard and M Davel and C van Heerden and N Kleynhans and K Bali},
- title = {Phone recognition for spoken web search},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{lu2022assessing,
+ title={Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography},
+ author={Lu, Yiqian and Yu, Zengjie and Liu, Jikui and An, Qi and Chen, Cong and Li, Ye and Wang, Yishan},
+ journal={Physiological Measurement},
+ volume={43},
+ number={7},
+ pages={075005},
+ year={2022},
+ publisher={IOP Publishing}
}
-@article{mediaeval00067,
- author = {C Penet and CH Demarty and G Gravier and P Gros},
- title = {Technicolor and inria/irisa at mediaeval 2011: learning temporal modality integration with bayesian networks},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{wei2022modality,
+ title={Modality and Event Adversarial Networks for Multi-Modal Fake News Detection},
+ author={Wei, Pengfei and Wu, Fei and Sun, Ying and Zhou, Hong and Jing, Xiao-Yuan},
+ journal={IEEE Signal Processing Letters},
+ volume={29},
+ pages={1382--1386},
+ year={2022},
+ publisher={IEEE}
}
-@article{mediaeval00068,
- author = {A Muscariello and G Gravier},
- title = {Irisa MediaEval 2011 Spoken Web Search System.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{yadav2023etma,
+ title={ETMA: Efficient Transformer-Based Multilevel Attention Framework for Multimodal Fake News Detection},
+ author={Yadav, Ashima and Gaba, Shivani and Khan, Haneef and Budhiraja, Ishan and Singh, Akansha and Singh, Krishna Kant},
+ journal={IEEE Transactions on Computational Social Systems},
+ year={2023},
+ publisher={IEEE}
}
-@article{mediaeval00069,
- author = {E Acar and S Spiegel and S Albayrak and DAI Labor},
- title = {MediaEval 2011 Affect Task: Violent Scene Detection combining audio and visual Features with SVM.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{ottl2022motilitai,
+ title={motilitAI: A machine learning framework for automatic prediction of human sperm motility},
+ author={Ottl, Sandra and Amiriparian, Shahin and Gerczuk, Maurice and Schuller, Bj{\"o}rn W},
+ journal={Iscience},
+ volume={25},
+ number={8},
+ year={2022},
+ publisher={Elsevier}
}
-@article{mediaeval00070,
- author = {F Krippner and G Meier and J Hartmann and R Knauf},
- title = {Placing media items using the Xtrieval Framework.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{chen2023multi,
+ title={Is multi-modal necessarily better? Robustness evaluation of multi-modal fake news detection},
+ author={Chen, Jinyin and Jia, Chengyu and Zheng, Haibin and Chen, Ruoxi and Fu, Chenbo},
+ journal={IEEE Transactions on Network Science and Engineering},
+ year={2023},
+ publisher={IEEE}
}
-@article{mediaeval00071,
- author = {M Morchid and G Linarès},
- title = {Mediaeval benchmark: Social Event Detection using LDA and external resources.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@inproceedings{tsai2022emvgan,
+ title={Emvgan: Emotion-aware music-video common representation learning via generative adversarial networks},
+ author={Tsai, Yu-Chih and Pan, Tse-Yu and Kao, Ting-Yang and Yang, Yi-Hsuan and Hu, Min-Chun},
+ booktitle={Proceedings of the 2022 International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia},
+ pages={13--18},
+ year={2022}
}
-@article{mediaeval00072,
- author = {H Glotin and J Razik and S Paris and JM Prevot},
- title = {Real-time entropic unsupervised violent scenes detection in Hollywood movies-DYNI@ MediaEval Affect Task 2011.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@phdthesis{harrando2022representation,
+ title={Representation, information extraction, and summarization for automatic multimedia understanding},
+ author={Harrando, Ismail},
+ year={2022},
+ school={Sorbonne Universit{\'e}}
}
-@article{mediaeval00073,
- author = {D Ferrés and H Rodríguez},
- title = {TALP at MediaEval 2011 Placing Task: Georeferencing Flickr videos with geographical knowledge and information retrieval.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{ahmad2022social,
+ title={Social media as an instant source of feedback on water quality},
+ author={Ahmad, Khubaib and Ayub, Muhammad Asif and Khan, J and Ahmad, N and Al-Fuqaha, A},
+ journal={IEEE Transactions on Technology and Society},
+ year={2022},
+ publisher={IEEE}
}
-@article{mediaeval00074,
- author = {B Safadi and G Quénot},
- title = {Lig at mediaeval 2011 affect task: use of a generic method},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@inproceedings{wu2022mm,
+ title={Mm-rec: Visiolinguistic model empowered multimodal news recommendation},
+ author={Wu, Chuhan and Wu, Fangzhao and Qi, Tao and Zhang, Chao and Huang, Yongfeng and Xu, Tong},
+ booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+ pages={2560--2564},
+ year={2022}
}
-@article{mediaeval00075,
- author = {M Rouvier and G Linarès},
- title = {LIA@ MediaEval 2011: Compact representation of heterogeneous descriptors for video genre classification.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@mastersthesis{patra2022deep,
+ title={Deep learning for automated polyp detection and localization in colonoscopy},
+ author={Patra, Amita},
+ year={2022},
+ school={OsloMet-storbyuniversitetet}
}
-@article{mediaeval00076,
- author = {S Rudinac and M Larson and A Hanjalic},
- title = {TUD-MIR at MediaEval 2011 Genre Tagging Task: Query expansion from a limited number of labeled videos.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@inproceedings{constantin2022two,
+ title={Two-Stage Spatio-Temporal Vision Transformer for the Detection of Violent Scenes},
+ author={Constantin, Mihai Gabriel and Ionescu, Bogdan},
+ booktitle={2022 14th International Conference on Communications (COMM)},
+ pages={1--5},
+ year={2022},
+ organization={IEEE}
}
-@article{mediaeval00077,
- author = {S Schmiedeke and P Kelm and T Sikora},
- title = {TUB@ MediaEval 2011 genre tagging task: prediction using bag-of-(visual)-words approaches},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@phdthesis{tovstogan2022exploration,
+ title={Exploration of music collections with audio embeddings},
+ author={Tovstogan, Philip and others},
+ year={2022},
+ school={Universitat Pompeu Fabra}
}
-@article{mediaeval00078,
- author = {K Schmidt and T Korner and S Heinich and T Wilhelm},
- title = {A Two-step Approach to Video Retrieval based on ASR transcriptions.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@inproceedings{storaas2022explainability,
+ title={Explainability methods for machine learning systems for multimodal medical datasets: research proposal},
+ author={Stor{\aa}s, Andrea M and Str{\"u}mke, Inga and Riegler, Michael A and Halvorsen, P{\aa}l},
+ booktitle={Proceedings of the 13th ACM Multimedia Systems Conference},
+ pages={347--351},
+ year={2022}
}
-@article{mediaeval00079,
- author = {R Aly and T Verschoor and R Ordelman},
- title = {UTwente does Rich Speech Retrieval at MediaEval 2011.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@inproceedings{cstefan2022overview,
+ title={Overview of imagecleffusion 2022 task-ensembling methods for media interestingness prediction and result diversification},
+ author={{\c{S}}tefan, Liviu-Daniel and Constantin, Mihai Gabriel and Dogariu, Mihai and Ionescu, Bogdan},
+ booktitle={CLEF2022 Working Notes, CEUR Workshop Proceedings, CEUR-WS. org, Bologna, Italy},
+ year={2022}
}
-@article{mediaeval00080,
- author = {C Wartena and M Larson},
- title = {Rich Speech Retrieval Using Query Word Filter.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@inproceedings{constantin2022ai,
+ title={Ai multimedia lab at imagecleffusion 2022: Deepfusion methods for ensembling in diverse scenarios},
+ author={Constantin, Mihai Gabriel and {\c{S}}tefan, Liviu-Daniel and Dogariu, Mihai and Ionescu, Bogdan},
+ booktitle={CLEF2022 Working Notes, CEUR Workshop Proceedings, CEUR-WS. org, Bologna, Italy},
+ year={2022}
}
-@article{mediaeval00081,
- author = {M Ruocco and H Ramampiaro},
- title = {Ntnu@ mediaeval2011: Social event detection task (sed)},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{ayoughi2023self,
+ title={Self-Contained Entity Discovery from Captioned Videos},
+ author={Ayoughi, Melika and Mettes, Pascal and Groth, Paul},
+ journal={ACM Transactions on Multimedia Computing, Communications and Applications},
+ volume={19},
+ number={5s},
+ pages={1--21},
+ year={2023},
+ publisher={ACM New York, NY}
}
-@article{mediaeval00082,
- author = {T Semela and HK Ekenel},
- title = {KIT at MediaEval 2011-Content-based genre classification on web-videos.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{zhang2023modularized,
+ title={Modularized composite attention network for continuous music emotion recognition},
+ author={Zhang, Meixian and Zhu, Yonghua and Zhang, Wenjun and Zhu, Yunwen and Feng, Tianyu},
+ journal={Multimedia Tools and Applications},
+ volume={82},
+ number={5},
+ pages={7319--7341},
+ year={2023},
+ publisher={Springer}
}
-@article{mediaeval00083,
- author = {R Tiwari and C Zhang and M Montes},
- title = {UAB at MediaEval 2011: Genre Tagging Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@incollection{jaisakthi2022social,
+ title={Social Media Flood Image Classification Using Transfer Learning with EfficientNet Variants},
+ author={Jaisakthi, SM and Dhanya, PR},
+ booktitle={Communication and Intelligent Systems: Proceedings of ICCIS 2021},
+ pages={759--770},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00084,
- author = {JM Perea-Ortega and A Montejo-Ráez and MC Díaz-Galiano and MT Martín-Valdivia},
- title = {Genre tagging of videos based on information retrieval and semantic similarity using WordNet.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{hosseini2023interpretable,
+ title={Interpretable fake news detection with topic and deep variational models},
+ author={Hosseini, Marjan and Sabet, Alireza Javadian and He, Suining and Aguiar, Derek},
+ journal={Online Social Networks and Media},
+ volume={36},
+ pages={100249},
+ year={2023},
+ publisher={Elsevier}
}
-@article{mediaeval00085,
- author = {W Alink and R Cornacchia},
- title = {Out-of-the-box strategy for Rich Speech Retrieval MediaEval 2011.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{pu2022semantic,
+ title={Semantic multimodal violence detection based on local-to-global embedding},
+ author={Pu, Yujiang and Wu, Xiaoyu and Wang, Shengjin and Huang, Yuming and Liu, Zihao and Gu, Chaonan},
+ journal={Neurocomputing},
+ volume={514},
+ pages={148--161},
+ year={2022},
+ publisher={Elsevier}
}
-@article{mediaeval00086,
- author = {M Eskevich and GJF Jones},
- title = {DCU at MediaEval 2011: Rich Speech Retrieval (RSR)},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@article{xia2022study,
+ title={Study on Music Emotion Recognition Based on the Machine Learning Model Clustering Algorithm},
+ author={Xia, Yu and Xu, Fumei and others},
+ journal={Mathematical Problems in Engineering},
+ volume={2022},
+ year={2022},
+ publisher={Hindawi}
}
-@article{mediaeval00087,
- author = {B Ionescu and K Seyerlehner and C Vertan and P Lambert},
- title = {Audio-Visual content description for video genre classification in the context of social media.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2011},
+
+@mastersthesis{jacobsen2022estimating,
+ title={Estimating Predictive Uncertainty in Gastrointestinal Image Segmentation},
+ author={Jacobsen, Felicia Ly},
+ year={2022}
}
-@article{mediaeval00088,
- author = {G Petkos and S Papadopoulos and Y Kompatsiaris},
- title = {Social event detection using multimodal clustering and integrating supervisory signals.},
- journal = {Proceedings of the ACM International Conference on Multimedia Retrieval},
- year = {2012},
+
+@inproceedings{wang2022ml,
+ title={ML-TFN: Multi Layers Tensor Fusion Network for Affective Video Content Analysis},
+ author={Wang, Qi and Xiang, Xiaohong and Zhao, Jun},
+ booktitle={International Conference on Neural Computing for Advanced Applications},
+ pages={184--196},
+ year={2022},
+ organization={Springer}
}
-@article{mediaeval00089,
- author = {F Metze and E Barnard and M Davel and C Van Heerden and X Anguera and G Gravier and N Rajput},
- title = {The spoken web search task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{akbar2022transboundary,
+ title={Transboundary Haze Prediction: Towards Time Series Forecasting of Cross-Data Analytics for Haze Prediction},
+ author={Akbar, Ali and Tahir, Muhammad Atif and Rafi, Muhammad},
+ booktitle={2022 International Conference on Emerging Trends in Smart Technologies (ICETST)},
+ pages={1--6},
+ year={2022},
+ organization={IEEE}
}
-@article{mediaeval00090,
- author = {C Hauff and GJ Houben},
- title = {Placing images on the world map: a microblog-based enrichment approach},
- journal = {Proceedings of the international ACM SIGIR},
- year = {2012},
+
+@article{jing2023multimodal,
+ title={Multimodal fake news detection via progressive fusion networks},
+ author={Jing, Jing and Wu, Hongchen and Sun, Jie and Fang, Xiaochang and Zhang, Huaxiang},
+ journal={Information processing \& management},
+ volume={60},
+ number={1},
+ pages={103120},
+ year={2023},
+ publisher={Elsevier}
}
-@article{mediaeval00091,
- author = {M Brenner and E Izquierdo},
- title = {Social event detection and retrieval in collaborative photo collections},
- journal = {Proceedings of the ACM International Conference on Multimedia Retrieval},
- year = {2012},
+
+@inproceedings{avramidis2023role,
+ title={On the Role of Visual Context in Enriching Music Representations},
+ author={Avramidis, Kleanthis and Stewart, Shanti and Narayanan, Shrikanth},
+ booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={1--5},
+ year={2023},
+ organization={IEEE}
}
-@article{mediaeval00092,
- author = {OAB Penatti and LT Li and J Almeida and R da S Torres},
- title = {A visual approach for video geocoding using bag-of-scenes},
- journal = {Proceedings of the ACM International Conference on Multimedia Retrieval},
- year = {2012},
+
+@inproceedings{muszynski2022impact,
+ title={Impact of aesthetic movie highlights on semantics and emotions: a preliminary analysis},
+ author={Muszynski, Michal and Morgenroth, Elenor and Vilaclara, Laura and Van De Ville, Dimitri and Vuilleumier, Patrik},
+ booktitle={Companion Publication of the 2022 International Conference on Multimodal Interaction},
+ pages={52--60},
+ year={2022}
}
-@article{mediaeval00093,
- author = {C Penet and CH Demarty and G Gravier and P Gros},
- title = {Multimodal information fusion and temporal integration for violence detection in movies},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2012},
+
+@article{chadebecq2023artificial,
+ title={Artificial intelligence and automation in endoscopy and surgery},
+ author={Chadebecq, Fran{\c{c}}ois and Lovat, Laurence B and Stoyanov, Danail},
+ journal={Nature Reviews Gastroenterology \& Hepatology},
+ volume={20},
+ number={3},
+ pages={171--182},
+ year={2023},
+ publisher={Nature Publishing Group UK London}
}
-@article{mediaeval00094,
- author = {Y Wang and H Sundaram and L Xie},
- title = {Social event detection with interaction graph modeling},
- journal = {Proceedings of the ACM international conference on Multimedia},
- year = {2012},
+
+@article{singh2023semi,
+ title={SEMI-FND: stacked ensemble based multimodal inferencing framework for faster fake news detection},
+ author={Singh, Prabhav and Srivastava, Ridam and Rana, KPS and Kumar, Vineet},
+ journal={Expert systems with applications},
+ volume={215},
+ pages={119302},
+ year={2023},
+ publisher={Elsevier}
}
-@article{mediaeval00095,
- author = {A Abad and RF Astudillo and I Trancoso},
- title = {The L2F Spoken Web Search system for Mediaeval 2012.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{xiao2022fine,
+ title={Fine-Grained Gastrointestinal Endoscopy Image Categorization},
+ author={Xiao, Peng and Gou, Pan and Wang, Bintao and Deng, Erqiang and Zhao, Pengbiao},
+ booktitle={Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences},
+ pages={239--242},
+ year={2022}
}
-@article{mediaeval00096,
- author = {E Schinas and G Petkos and S Papadopoulos and Y Kompatsiaris},
- title = {CERTH@ MediaEval 2012 Social Event Detection Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{de2022experiences,
+ title={Experiences from the MediaEval Predicting Media Memorability Task},
+ author={de Herrera, Alba Garc{\'\i}a Deco and Constantin, Mihai Gabriel and Demarty, Chaire-H{\'e}l{\`e}ne and Fosco, Camilo and Halder, Sebastian and Healy, Graham and Ionescu, Bogdan and Matran-Fernandez, Ana and Smeaton, Alan F and Sultana, Mushfika and others},
+ journal={arXiv preprint arXiv:2212.03955},
+ year={2022}
}
-@article{mediaeval00046,
- author = {AL Baulida},
- title = {Semantic and Diverse Summarization of Egocentric Photo Events},
- journal = {M. Sc. Visions and Robotics Thesis},
- year = {2012}
+
+@article{elsaeed2021detecting,
+ title={Detecting fake news in social media using voting classifier},
+ author={Elsaeed, Eman and Ouda, Osama and Elmogy, Mohammed M and Atwan, Ahmed and El-Daydamony, Eman},
+ journal={IEEE Access},
+ volume={9},
+ pages={161909--161925},
+ year={2021},
+ publisher={IEEE}
}
-@article{mediaeval00097,
- author = {C Hauff and GJ Houben},
- title = {Geo-location estimation of flickr images: Social web based enrichment},
- journal = {European Conference on Information Retrieval},
- year = {2012},
+
+@article{hu2022mmnet,
+ title={MMNet: Multi-modal Fusion with Mutual Learning Network for Fake News Detection},
+ author={Hu, Linmei and Zhao, Ziwang and Ge, Xinkai and Song, Xuemeng and Nie, Liqiang},
+ journal={arXiv preprint arXiv:2212.05699},
+ year={2022}
}
-@article{mediaeval00098,
- author = {H Lei and J Choi and G Friedland},
- title = {Multimodal city-verification on flickr videos using acoustic and textual features},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2012},
+
+@inproceedings{wang2022multimodal,
+ title={Multimodal Content Veracity Assessment with Bidirectional Transformers and Self-Attention-based Bi-GRU Networks},
+ author={Wang, Jenq-Haur and Norouzi, Mehdi and Tsai, Shu Ming},
+ booktitle={2022 IEEE Eighth International Conference on Multimedia Big Data (BigMM)},
+ pages={133--137},
+ year={2022},
+ organization={IEEE}
}
-@article{mediaeval00099,
- author = {J Choi and G Friedland and V Ekambaram and K Ramchandran},
- title = {Multimodal location estimation of consumer media: Dealing with sparse training data},
- journal = {IEEE International Conference on Multimedia and Expo},
- year = {2012},
+
+@article{zhang2022bert,
+ title={BERT Based Fake News Detection Model},
+ author={Zhang, Yang and Shao, Yi and Zhang, Xuan and Wan, Wenbo and Li, Jing and Sun, Jiande},
+ journal={Training},
+ volume={1530},
+ pages={383},
+ year={2022}
}
-@article{mediaeval00100,
- author = {M Eskevich and GJF Jones and C Wartena and M Larson and R Aly and T Verschoor and R Ordelman},
- title = {Comparing retrieval effectiveness of alternative content segmentation methods for internet video search.},
- journal = {International Workshop on Content-Based Multimedia Indexing},
- year = {2012},
+
+@inproceedings{moriya2023improving,
+ title={Improving Noise Robustness for Spoken Content Retrieval Using Semi-Supervised ASR and N-Best Transcripts for BERT-Based Ranking Models},
+ author={Moriya, Yasufumi and Jones, Gareth JF},
+ booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
+ pages={398--405},
+ year={2023},
+ organization={IEEE}
}
-@article{mediaeval00101,
- author = {GJF Jones},
- title = {An introduction to crowdsourcing for language and multimedia technology research},
- journal = {PROMISE Winter School},
- year = {2012},
+
+@article{kumari2021amfb,
+ title={Amfb: Attention based multimodal factorized bilinear pooling for multimodal fake news detection},
+ author={Kumari, Rina and Ekbal, Asif},
+ journal={Expert Systems with Applications},
+ volume={184},
+ pages={115412},
+ year={2021},
+ publisher={Elsevier}
}
-@article{mediaeval00102,
- author = {C Wartena},
- title = {Comparing segmentation strategies for efficient video passage retrieval},
- journal = {IEEE International Workshop on Content-Based Multimedia Indexing (CBMI)},
- year = {2012},
+
+@phdthesis{di2021uso,
+ title={O Uso de Infer{\^e}ncia Variacional para Reconhecimento de Emo{\c{c}}oes em M{\'u}sica},
+ author={di Domenico, Nathalie Fernanda Toffani Magliano},
+ year={2021},
+ school={Universidade Federal do Rio de Janeiro}
}
-@article{mediaeval00103,
- author = {LT Li and J Almeida and DCG Pedronette and OAB Penatti and R da S Torres},
- title = {A multimodal approach for video geocoding.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{bassmandetecting,
+ title={Detecting Flooding in Social Media Imagery Using Multimodal Deep Learning},
+ author={Bassman, Tamika J and Hanif, Usman and Xia, Evelyn}
}
-@article{mediaeval00104,
- author = {P Kelm and S Schmiedeke and T Sikora},
- title = {How Spatial Segmentation improves the Multimodal Geo-Tagging.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{yuan2021improving,
+ title={Improving fake news detection with domain-adversarial and graph-attention neural network},
+ author={Yuan, Hua and Zheng, Jie and Ye, Qiongwei and Qian, Yu and Zhang, Yan},
+ journal={Decision Support Systems},
+ volume={151},
+ pages={113633},
+ year={2021},
+ publisher={Elsevier}
}
-@article{mediaeval00105,
- author = {N Derbas and F Thollard and B Safadi and G Quénot},
- title = {Lig at mediaeval 2012 affect task: use of a generic method.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{zhang2021music,
+ title={Music emotion recognition based on combination of multiple features and neural network},
+ author={Zhang, Chenguang and Yu, Jinming and Chen, Zhuang},
+ booktitle={2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)},
+ volume={4},
+ pages={1461--1465},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00106,
- author = {J Schlater and B Ionescu and I Mironica and M Schedl},
- title = {ARF@ MediaEval 2012: An Uninformed Approach to Violence Detection in Hollywood Movies.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{dao2021image,
+ title={Image-2-aqi: Aware of the surrounding air qualification by a few images},
+ author={Dao, Minh-Son and Zettsu, Koji and Rage, Uday Kiran},
+ booktitle={Advances and Trends in Artificial Intelligence. From Theory to Practice: 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26--29, 2021, Proceedings, Part II 34},
+ pages={335--346},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00107,
- author = {F Eyben and F Weninger and N Lehment and G Rigoll and B Schuller},
- title = {Violent scenes detection with large, brute-forced acoustic and visual feature sets.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{li2021entity,
+ title={Entity-oriented multi-modal alignment and fusion network for fake news detection},
+ author={Li, Peiguang and Sun, Xian and Yu, Hongfeng and Tian, Yu and Yao, Fanglong and Xu, Guangluan},
+ journal={IEEE Transactions on Multimedia},
+ volume={24},
+ pages={3455--3468},
+ year={2021},
+ publisher={IEEE}
}
-@article{mediaeval00108,
- author = {M Zeppelzauer and M Zaharieva and C Breiteneder},
- title = {A Generic Approach for Social Event Detection in Large Photo Collections.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{wu2021multimodal,
+ title={Multimodal fusion with co-attention networks for fake news detection},
+ author={Wu, Yang and Zhan, Pengwei and Zhang, Yunjian and Wang, Liming and Xu, Zhen},
+ booktitle={Findings of the association for computational linguistics: ACL-IJCNLP 2021},
+ pages={2560--2569},
+ year={2021}
}
-@article{mediaeval00109,
- author = {M Brenner and E Izquierdo},
- title = {QMUL@ MediaEval 2012: Social Event Detection in Collaborative Photo Collections.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{ahmadmodified,
+ title={A Modified Memory-Efficient U-Net for Segmentation of Polyps},
+ author={Ahmad, Asif and Badshah, Noor and Hassan, Mahmood Ul}
}
-@article{mediaeval00110,
- author = {LT Li and DCG Pedronette and J Almeida and OA Penatti and RT Calumby and R da S Torres},
- title = {Multimedia multimodal geocoding},
- journal = {ACM International Conference on Advances in Geographic Information Systems},
- year = {2012},
+
+@inproceedings{hariharan2021hybrid,
+ title={Hybrid Approach for Effective Disaster Management Using Twitter Data and Image-Based Analysis},
+ author={Hariharan, Kartick and Lobo, Ashley and Deshmukh, Sujata},
+ booktitle={2021 International Conference on Communication information and Computing Technology (ICCICT)},
+ pages={1--6},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00111,
- author = {E Acar and S Albayrak},
- title = {DAI Lab at MediaEval 2012 Affect Task: The Detection of Violent Scenes using Affective Features.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{10.1145/3465481.3470059,
+author = {Ad\~{a}o Teixeira, Marcos Vin\'{\i}cius and Avila, Sandra},
+title = {What should we pay attention to when classifying violent videos?},
+year = {2021},
+isbn = {9781450390514},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3465481.3470059},
+doi = {10.1145/3465481.3470059},
+booktitle = {Proceedings of the 16th International Conference on Availability, Reliability and Security},
+articleno = {49},
+numpages = {10},
+keywords = {attention models, deep neural networks, violence classification},
+location = {Vienna, Austria},
+series = {ARES '21}
}
-@article{mediaeval00112,
- author = {M Eskevich and GJF Jones and M Larson and R Ordelman},
- title = {Creating a data collection for evaluating rich speech retrieval},
- journal = {International conference on Language Resources and Evaluation (LREC)},
- year = {2012},
+
+@inproceedings{mohammed20215w1h,
+ title={5W1H Aware Framework for Representing and Detecting Real Events from Multimedia Digital Ecosystem},
+ author={Mohammed, Siraj and Getahun, Fekade and Chbeir, Richard},
+ booktitle={European Conference on Advances in Databases and Information Systems},
+ pages={57--70},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00113,
- author = {X Li and C Hauff and M Larson and A Hanjalic},
- title = {Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012}
+
+@ARTICLE{9439825,
+ author={Koutini, Khaled and Eghbal-zadeh, Hamid and Widmer, Gerhard},
+ journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
+ title={Receptive Field Regularization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks},
+ year={2021},
+ volume={29},
+ number={},
+ pages={1987-2000},
+ keywords={Radio frequency;Computer architecture;Task analysis;Neurons;Tagging;Speech processing;Feature extraction;Convolutional neural networks;receptive field regularization;acoustic scene classification;instrument detection;emotion detection},
+ doi={10.1109/TASLP.2021.3082307}}
+
+@INPROCEEDINGS{9506411,
+ author={Leyva, Roberto and Sanchez, Victor},
+ booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
+ title={Video Memorability Prediction Via Late Fusion Of Deep Multi-Modal Features},
+ year={2021},
+ volume={},
+ number={},
+ pages={2488-2492},
+ keywords={Visualization;Fuses;Social networking (online);Computational modeling;Conferences;Neural networks;Feature extraction;Video memorability prediction;video analysis;multi-modal feature processing;fusion},
+ doi={10.1109/ICIP42928.2021.9506411}}
+
+@INPROCEEDINGS{9428404,
+ author={Chen, Jiaxin and Wu, Zekai and Yang, Zhenguo and Xie, Haoran and Wang, Fu Lee and Liu, Wenyin},
+ booktitle={2021 IEEE International Conference on Multimedia and Expo (ICME)},
+ title={Multimodal Fusion Network with Latent Topic Memory for Rumor Detection},
+ year={2021},
+ volume={},
+ number={},
+ pages={1-6},
+ keywords={Visualization;Social networking (online);Conferences;Semantics;Multimodal fusion;Self-attentive;Rumor detection},
+ doi={10.1109/ICME51207.2021.9428404}}
+
+@INPROCEEDINGS{9461913,
+ author={Knox, Dillon and Greer, Timothy and Ma, Benjamin and Kuo, Emily and Somandepalli, Krishna and Narayanan, Shrikanth},
+ booktitle={2021 International Conference on Content-Based Multimedia Indexing (CBMI)},
+ title={Loss Function Approaches for Multi-label Music Tagging},
+ year={2021},
+ volume={},
+ number={},
+ pages={1-4},
+ keywords={Emotion recognition;Neural networks;Music;Tagging;Predictive models;Convolutional neural networks;Task analysis;music tagging;loss functions;multi-label deep learning;convolutional neural networks},
+ doi={10.1109/CBMI50038.2021.9461913}}
+
+@misc{yang2021superb,
+ title={SUPERB: Speech processing Universal PERformance Benchmark},
+ author={Shu-wen Yang and Po-Han Chi and Yung-Sung Chuang and Cheng-I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan-Ting Lin and Tzu-Hsien Huang and Wei-Cheng Tseng and Ko-tik Lee and Da-Rong Liu and Zili Huang and Shuyan Dong and Shang-Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung-yi Lee},
+ year={2021},
+ eprint={2105.01051},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
}
-@article{mediaeval00114,
- author = {A Varona and M Penagarikano and LJ Rodríguez-Fuentes and G Bordel and M Diez},
- title = {GTTS System for the Spoken Web Search Task at MediaEval 2012.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{pustu2021visualizing,
+ title={Visualizing Copyright-Protected Video Archive Content Through Similarity Search},
+ author={Pustu-Iren, Kader and M{\"u}ller-Budack, Eric and Hakimov, Sherzod and Ewerth, Ralph},
+ booktitle={International Conference on Theory and Practice of Digital Libraries},
+ pages={123--127},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00115,
- author = {MS Dao and TV Nguyen and G Boato and FGB De Natale},
- title = {The Watershed-based Social Events Detection Method with Support from External Data Sources.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{yogapriya2021gastrointestinal,
+ title={Gastrointestinal tract disease classification from wireless endoscopy images using pretrained deep learning model},
+ author={Yogapriya, J and Chandran, Venkatesan and Sumithra, MG and Anitha, P and Jenopaul, P and Suresh Gnana Dhas, C},
+ journal={Computational and mathematical methods in medicine},
+ volume={2021},
+ year={2021},
+ publisher={Hindawi}
}
-@article{mediaeval00116,
- author = {YG Jiang and Q Dai and CC Tan and X Xue and CW Ngo},
- title = {The Shanghai-Hongkong Team at MediaEval2012: Violent Scene Detection Using Trajectory-based Features.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{mohamed2021music,
+ title={MUSIC RECOMMENDATION SYSTEM USED EMOTIONS TO TRACK AND CHANGE NEGATIVE USERS’MOOD},
+ author={MOHAMED, MARWA HUSSIEN and KHAFAGY, MOHAMED HELMY and HASAN, MOHAMED},
+ journal={Journal of Theoretical and Applied Information Technology},
+ volume={99},
+ number={17},
+ year={2021}
}
-@article{mediaeval00117,
- author = {S Schmiedeke and P Kelm and T Sikora},
- title = {Cross-modal categorisation of user-generated video sequences},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2012},
+
+@article{scalespredicting,
+ title={Predicting Short-term Media Memorability from Captions},
+ author={Scales, Luke}
}
-@article{mediaeval00118,
- author = {C Penet and CH Demarty and M Soleymani and G Gravier and P Gros},
- title = {Technicolor/inria/imperial college london at the mediaeval 2012 violent scene detection task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@INPROCEEDINGS{9637554,
+ author={Jiang, Jian and Zhang, Hua},
+ booktitle={2021 International Conference on Culture-oriented Science & Technology (ICCST)},
+ title={Research on the Method of Cross-modal Affective Association in Audiovisual},
+ year={2021},
+ volume={},
+ number={},
+ pages={38-42},
+ keywords={Emotion recognition;Correlation;Semantics;Transforms;Data models;Emotional responses;cross-modal;affective correlation;music and image;Audiovisual;isomorphic space},
+ doi={10.1109/ICCST53801.2021.00019}}
+
+@INPROCEEDINGS{9647169,
+ author={Jony, Rabiul Islam and Woodley, Alan and Perrin, Dimitri},
+ booktitle={2021 Digital Image Computing: Techniques and Applications (DICTA)},
+ title={Flood Detection in Social Media Using Multimodal Fusion on Multilingual Dataset},
+ year={2021},
+ volume={},
+ number={},
+ pages={01-08},
+ keywords={Training;Visualization;Social networking (online);Fuses;Digital images;Design methodology;Machine learning},
+ doi={10.1109/DICTA52665.2021.9647169}}
+
+@article{varshney2022unified,
+ title={A unified approach of detecting misleading images via tracing its instances on web and analyzing its past context for the verification of multimedia content},
+ author={Varshney, Deepika and Vishwakarma, Dinesh Kumar},
+ journal={International Journal of Multimedia Information Retrieval},
+ volume={11},
+ number={3},
+ pages={445--459},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00119,
- author = {O Van Laere and S Schockaert and J Quinn and FC Langbein and B Dhoedt},
- title = {Ghent and cardiff university at the 2012 placing task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{tirupattur2021violence,
+ title={Violence Detection in Videos},
+ author={Tirupattur, Praveen and Schulze, Christian and Dengel, Andreas},
+ journal={arXiv preprint arXiv:2109.08941},
+ year={2021}
}
-@article{mediaeval00120,
- author = {J Choi and VN Ekambaram and G Friedland and K Ramchandran},
- title = {The 2012 ICSI/Berkeley Video Location Estimation System.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@INPROCEEDINGS{9533916,
+ author={Roy, Arjun and Ekbal, Asif},
+ booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
+ title={MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification},
+ year={2021},
+ volume={},
+ number={},
+ pages={1-8},
+ keywords={Forensics;Neural networks;Medical services;Manuals;Benchmark testing;Robustness;Planning;multimodal;multi-lingual;fact verification},
+ doi={10.1109/IJCNN52387.2021.9533916}}
+
+@inproceedings{godwin2021evaluating,
+ title={Evaluating deep music generation methods using data augmentation},
+ author={Godwin, Toby and Rizos, Georgios and Baird, Alice and Al Futaisi, Najla D and Brisse, Vincent and Schuller, Bj{\"o}rn W},
+ booktitle={2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)},
+ pages={1--6},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00121,
- author = {T De Nies and P Debevere and D Van Deursen and W De Neve and E Mannens and R Van de Walle},
- title = {Ghent University-IBBT at MediaEval 2012 Search and Hyperlinking: Semantic Similarity using Named Entities.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{orjesek2022end,
+ title={End-to-end music emotion variation detection using iteratively reconstructed deep features},
+ author={Orjesek, Richard and Jarina, Roman and Chmulik, Michal},
+ journal={Multimedia Tools and Applications},
+ volume={81},
+ number={4},
+ pages={5017--5031},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00122,
- author = {S Diplaris and G Petkos and S Papadopoulos and Y Kompatsiaris and N Sarris and C Martin and A Goker and D Corney and J Geurts and Y Liu and JC Point},
- title = {Social sensor: Surfacing real-time trends and insights from multiple social networks.},
- journal = {NEM Summit Proceedings},
- year = {2012},
+
+@INPROCEEDINGS{9667614,
+ author={Jaiswal, Ramji and Singh, Upendra Pratap and Singh, Krishna Pratap},
+ booktitle={2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)},
+ title={Fake News Detection Using BERT-VGG19 Multimodal Variational Autoencoder},
+ year={2021},
+ volume={},
+ number={},
+ pages={1-5},
+ keywords={Visualization;Social networking (online);Blogs;Bit error rate;Natural languages;Feature extraction;Data models;Fake News;Modality;Variational Autoencoder;Encoder;Decoder;Latent Representation},
+ doi={10.1109/UPCON52273.2021.9667614}}
+
+@INPROCEEDINGS{9712676,
+ author={Liu, Rui and Wu, Xiaoyu},
+ booktitle={2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)},
+ title={Multimodal Attention Network for Violence Detection},
+ year={2022},
+ volume={},
+ number={},
+ pages={503-506},
+ keywords={Visualization;Computer vision;Focusing;Feature extraction;Data mining;Task analysis;Optical flow;multimodal;motion feature;attention;violence detection},
+ doi={10.1109/ICCECE54139.2022.9712676}}
+
+@inproceedings{brown2021automated,
+ title={Automated video labelling: Identifying faces by corroborative evidence},
+ author={Brown, Andrew and Coto, Ernesto and Zisserman, Andrew},
+ booktitle={2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)},
+ pages={77--83},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00123,
- author = {OAB Penatti and E Valle and RS Torres},
- title = {Image and video representations based on visual dictionaries},
- journal = {Ph. D. Thesis, Universidade Estadual de Campinas},
- year = {2012},
+
+@mastersthesis{maia2020assessing,
+ title={Assessing the emotional impact of video using machine learning techniques},
+ author={Maia, Andr{\'e} Filipe Lopes},
+ year={2020}
}
-@article{mediaeval00124,
- author = {J Fleureau and C Penet and P Guillotel and CH Demarty},
- title = {Electrodermal activity applied to violent scenes impact measurement and user profiling.},
- journal = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
- year = {2012},
+
+@article{cheema2021role,
+ title={On the role of images for analyzing claims in social media},
+ author={Cheema, Gullal S and Hakimov, Sherzod and M{\"u}ller-Budack, Eric and Ewerth, Ralph},
+ journal={arXiv preprint arXiv:2103.09602},
+ year={2021}
}
-@article{mediaeval00125,
- author = {M Ruocco},
- title = {Context-aware image semantic extraction in the social web},
- journal = {ACM International Conference on World Wide Web},
- year = {2012},
+
+@inproceedings{tuan2021multimodal,
+ title={Multimodal fusion with BERT and attention mechanism for fake news detection},
+ author={Tuan, Nguyen Manh Duc and Minh, Pham Quang Nhat},
+ booktitle={2021 RIVF International Conference on Computing and Communication Technologies (RIVF)},
+ pages={1--6},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00126,
- author = {D Nadeem and R Aly and R Ordelman},
- title = {UTwente does Brave New Tasks for MediaEval 2012: Searching and Hyperlinking.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{sweeney2021influence,
+ title={The influence of audio on video memorability with an audio gestalt regulated video memorability system},
+ author={Sweeney, Lorin and Healy, Graham and Smeaton, Alan F},
+ booktitle={2021 International Conference on Content-Based Multimedia Indexing (CBMI)},
+ pages={1--6},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00127,
- author = {A Badii and M Einig},
- title = {MediaEval 2012 Visual Privacy Task: Privacy, Intelligibility through Pixellation and Edge Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{kordopatis2021leveraging,
+ title={Leveraging efficientnet and contrastive learning for accurate global-scale location estimation},
+ author={Kordopatis-Zilos, Giorgos and Galopoulos, Panagiotis and Papadopoulos, Symeon and Kompatsiaris, Ioannis},
+ booktitle={Proceedings of the 2021 International Conference on Multimedia Retrieval},
+ pages={155--163},
+ year={2021}
}
-@article{mediaeval00128,
- author = {JC Lei and G Friedland},
- title = {Multimodal city-identification on flickr videos using acoustic and textual features},
- journal = {Proceedings of the ACM international conference on Multimedia},
- year = {2012},
+
+@article{singh2022predicting,
+ title={Predicting image credibility in fake news over social media using multi-modal approach},
+ author={Singh, Bhuvanesh and Sharma, Dilip Kumar},
+ journal={Neural Computing and Applications},
+ volume={34},
+ number={24},
+ pages={21503--21517},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00129,
- author = {AI Oviedo and O Ortega and JM Perea-Ortega and E Sanchis},
- title = {Video clustering based on the collaboration of multimedia clusterers.},
- journal = {Conferencia Latinoamericana En Informatica (CLEI)},
- year = {2012},
+
+@article{hassan2021visual,
+ title={Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021},
+ author={Hassan, Syed Zohaib and Ahmad, Kashif and Riegler, Michael A and Hicks, Steven and Conci, Nicola and Halvorsen, P{\aa}l and Al-Fuqaha, Ala},
+ journal={arXiv preprint arXiv:2111.11471},
+ year={2021}
}
-@article{mediaeval00130,
- author = {M Florian and R Nitendra and A Xavier and D Marelie and ...},
- title = {The spoken web search task at MediaEval 2011},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@article{raj2022people,
+ title={People lie, actions Don't! Modeling infodemic proliferation predictors among social media users},
+ author={Raj, Chahat and Meel, Priyanka},
+ journal={Technology in Society},
+ volume={68},
+ pages={101930},
+ year={2022},
+ publisher={Elsevier}
}
-@article{mediaeval00131,
- author = {M Eskevich and GJF Jones},
- title = {DCU search runs at MediaEval 2012: search and hyperlinking task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2012},
+
+@inproceedings{panagiotopoulos2022leveraging,
+ title={Leveraging Selective Prediction for Reliable Image Geolocation},
+ author={Panagiotopoulos, Apostolos and Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon},
+ booktitle={International Conference on Multimedia Modeling},
+ pages={369--381},
+ year={2022},
+ organization={Springer}
}
-@article{mediaeval00132,
- author = {J Choi},
- title = {A Multimodal Approach to Automatic Geo-Tagging of Video},
- year = {2012},
+
+@article{yange2021violence,
+ title={Violence Detection in Ranches Using Computer Vision and Convolution Neural Network},
+ author={Yange, Terungwa Simon and Egbunu, Charity Ojochogwu and Onyekware, Oluoha and Rufai, Malik Adeiza and Godwin, Comfort},
+ journal={Journal of Computer Scine and Information Technology},
+ pages={94--104},
+ year={2021}
}
-@article{mediaeval00133,
- author = {F Eyben and F Weninger and S Squartini and B Schuller},
- title = {Real-life voice activity detection with lstm recurrent neural networks and an application to hollywood movies.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing},
- year = {2013},
+
+@article{algiriyage2022multi,
+ title={Multi-source multimodal data and deep learning for disaster response: a systematic review},
+ author={Algiriyage, Nilani and Prasanna, Raj and Stock, Kristin and Doyle, Emma EH and Johnston, David},
+ journal={SN Computer Science},
+ volume={3},
+ pages={1--29},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00134,
- author = {T Reuter and S Papadopoulos and G Petkos and V Mezaris and Y Kompatsiaris and P Cimiano and C de Vries and S Geva},
- title = {Social event detection at mediaeval 2013: Challenges, datasets, and evaluation},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{elsaeed2021detecting,
+ title={Detecting fake news in social media using voting classifier},
+ author={Elsaeed, Eman and Ouda, Osama and Elmogy, Mohammed M and Atwan, Ahmed and El-Daydamony, Eman},
+ journal={IEEE Access},
+ volume={9},
+ pages={161909--161925},
+ year={2021},
+ publisher={IEEE}
}
-@article{mediaeval00135,
- author = {F Metze and X Anguera and E Barnard and G Gravier},
- title = {The spoken web search task at MediaEval 2012.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{tan2021semi,
+ title={Semi-supervised music emotion recognition using noisy student training and harmonic pitch class profiles},
+ author={Tan, Hao Hao},
+ journal={arXiv preprint arXiv:2112.00702},
+ year={2021}
}
-@article{mediaeval00136,
- author = {M Eskevich and GJF Jones and R Aly and RJF Ordelman and S Chen and D Nadeem and C Guinaudeau and G Gravier and P S{\'e}billot and T De Nies and others},
- title = {Multimedia information seeking through search and hyperlinking.},
- journal = {ACM International conference on multimedia retrieval},
- year = {2013},
+
+@inproceedings{wu2021cross,
+ title={Cross-modal attention network with orthogonal latent memory for rumor detection},
+ author={Wu, Zekai and Chen, Jiaxin and Yang, Zhenguo and Xie, Haoran and Wang, Fu Lee and Liu, Wenyin},
+ booktitle={Web Information Systems Engineering--WISE 2021: 22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26--29, 2021, Proceedings, Part I 22},
+ pages={527--541},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00137,
- author = {X Anguera and F Metze and A Buzo and I Szoke and LJ Rodriguez-Fuentes},
- title = {The spoken web search task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{kiziltepe2021annotated,
+ title={An annotated video dataset for computing video memorability},
+ author={Kiziltepe, Rukiye Savran and Sweeney, Lorin and Constantin, Mihai Gabriel and Doctor, Faiyaz and de Herrera, Alba Garc{\'\i}a Seco and Demarty, Claire-H{\'e}l{\'e}ne and Healy, Graham and Ionescu, Bogdan and Smeaton, Alan F},
+ journal={Data in Brief},
+ volume={39},
+ pages={107671},
+ year={2021},
+ publisher={Elsevier}
}
-@article{mediaeval00138,
- author = {H Wang and T Lee and CC Leung and B Ma and H Li},
- title = {Using parallel tokenizers with DTW matrix combination for low-resource spoken term detection.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing},
- year = {2013},
+
+@article{kiziltepe2021annotated,
+ title={An annotated video dataset for computing video memorability},
+ author={Kiziltepe, Rukiye Savran and Sweeney, Lorin and Constantin, Mihai Gabriel and Doctor, Faiyaz and de Herrera, Alba Garc{\'\i}a Seco and Demarty, Claire-H{\'e}l{\'e}ne and Healy, Graham and Ionescu, Bogdan and Smeaton, Alan F},
+ journal={Data in Brief},
+ volume={39},
+ pages={107671},
+ year={2021},
+ publisher={Elsevier}
}
-@article{mediaeval00139,
- author = {LJ Rodriguez-Fuentes and M Penagarikano},
- title = {Mediaeval 2013 spoken web search task: System performance measures.},
- journal = {Technical report},
- year = {2013},
+
+@inproceedings{zhang2021exploring,
+ title={Exploring fusion strategies in deep learning models for multi-modal classification},
+ author={Zhang, Duoyi and Nayak, Richi and Bashar, Md Abul},
+ booktitle={Australasian Conference on Data Mining},
+ pages={102--117},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00140,
- author = {X Anguera and M Ferrarons},
- title = {Memory efficient subsequence DTW for query-by-example spoken term detection},
- journal = {IEEE International Conference on Multimedia and Expo (ICME)},
- year = {2013},
+
+@article{alonso2021multimodal,
+ title={Multimodal fake news detection},
+ author={Alonso-Bartolome, Santiago and Segura-Bedmar, Isabel},
+ journal={arXiv preprint arXiv:2112.04831},
+ year={2021}
}
-@article{mediaeval00141,
- author = {A Abad and LJ Rodriguez-Fuentes and M Penagarikano and A Varona and G Bordel},
- title = {On the calibration and fusion of heterogeneous spoken term detection systems.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{manco2022learning,
+ title={Learning music audio representations via weak language supervision},
+ author={Manco, Ilaria and Benetos, Emmanouil and Quinton, Elio and Fazekas, Gy{\"o}rgy},
+ booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={456--460},
+ year={2022},
+ organization={IEEE}
}
-@article{mediaeval00142,
- author = {KN Vavliakis and AL Symeonidis and PA Mitkas},
- title = {Event identification in web social media through named entity recognition and topic modeling},
- journal = {Data and Knowledge Engineering},
- year = {2013},
+
+@article{kiziltepe2021overview,
+ title={Overview of the MediaEval 2021 predicting media memorability task},
+ author={Kiziltepe, Rukiye Savran and Constantin, Mihai Gabriel and Demarty, Claire-H{\'e}l{\`e}ne and Healy, Graham and Fosco, Camilo and de Herrera, Alba Garc{\'\i}a Seco and Halder, Sebastian and Ionescu, Bogdan and Matran-Fernandez, Ana and Smeaton, Alan F and others},
+ journal={arXiv preprint arXiv:2112.05982},
+ year={2021}
}
-@article{mediaeval00143,
- author = {M Saraclar and A Sethy and B Ramabhadran and L Mangu and J Cui and X Cui and B Kingsbury and J Mamou},
- title = {An empirical study of confusion modeling in keyword search for low resource languages.},
- journal = {IEEE Workshop on Automatic Speech Recognition and Understanding},
- year = {2013},
+
+@article{takashima2023embedding,
+ title={Embedding-based Music Emotion Recognition Using Composite Loss},
+ author={Takashima, Naoki and Li, Fr{\'e}d{\'e}ric and Grzegorzek, Marcin and Shirahama, Kimiaki},
+ journal={IEEE Access},
+ year={2023},
+ publisher={IEEE}
}
-@article{mediaeval00144,
- author = {O Van Laere and S Schockaert and B Dhoedt},
- title = {Georeferencing Flickr resources based on textual meta-data},
- journal = {Information Sciences},
- year = {2013},
+
+@article{martin2021spatio,
+ title={Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark},
+ author={Martin, Pierre-Etienne},
+ journal={arXiv preprint arXiv:2112.12074},
+ year={2021}
}
-@article{mediaeval00145,
- author = {C Hauff},
- title = {A study on the accuracy of Flickr's geotag data},
- journal = {Proceedings of the international ACM SIGIR},
- year = {2013},
+
+@article{zahra2021two,
+ title={Two stream network for stroke detection in table tennis},
+ author={Zahra, Anam and Martin, Pierre-Etienne},
+ journal={arXiv preprint arXiv:2112.12073},
+ year={2021}
}
-@article{mediaeval00146,
- author = {B Ionescu and J Schluter and I Mironica and M Schedl},
- title = {A naive mid-level concept-based fusion approach to violence detection in hollywood movies},
- journal = {ACM International conference on multimedia retrieval},
- year = {2013},
+
+@article{sweeney2021predicting,
+ title={Predicting media memorability: comparing visual, textual and auditory features},
+ author={Sweeney, Lorin and Healy, Graham and Smeaton, Alan F},
+ journal={arXiv preprint arXiv:2112.07969},
+ year={2021}
}
-@article{mediaeval00147,
- author = {BK Bao and W Min and K Lu and C Xu},
- title = {Social event detection with robust high-order co-clustering},
- journal = {Proceedings of the 3rd ACM conference on …},
- year = {2013},
+
+@inproceedings{el2021detecting,
+ title={Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning},
+ author={El Vaigh, Cheikh Brahim and Girault, Thomas and Mallart, Cyrielle and Nguyen, Duc Hau},
+ booktitle={MediaEval 2021-MediaEval Multimedia Evaluation benchmark. Workshop},
+ pages={1--3},
+ year={2021}
}
-@article{mediaeval00148,
- author = {A Popescu and N Ballas},
- title = {CEA LIST's Participation at MediaEval 2013 Placing Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{ahmad2021deep,
+ title={Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content},
+ author={Ahmad, Khubaib and Ayub, Muhammad Asif and Ahmad, Kashif and Al-Fuqaha, Ala and Ahmad, Nasir},
+ journal={arXiv preprint arXiv:2112.12060},
+ year={2021}
}
-@article{mediaeval00149,
- author = {PK Lanchantin and PJ Bell and MJ Gales and T Hain and X Liu and Y Long and J Quinnell and S Renals and O Saz and MS Seigel and P Swietojanski and PC Woodland},
- title = {Automatic transcription of multi-genre media archives.},
- year = {2013},
+
+@article{zhou2021dl,
+ title={DL-TXST NewsImages: Contextual Feature Enrichment for Image-Text Rematching},
+ author={Zhou, Yuxiao and Gonzalez, Andres and Tabassum, Parisa and Tesic, Jelena},
+ journal={Proceedings of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
+ year={2021}
}
-@article{mediaeval00150,
- author = {M Zaharieva and M Zeppelzauer and C Breiteneder},
- title = {Automated social event detection in large photo collections.},
- journal = {ACM International conference on multimedia retrieval},
- year = {2013},
+
+@inproceedings{shebaro2021dl,
+ title={DL-TXST fake news: Enhancing tweet content classification with adapted language models},
+ author={Shebaro, Muhieddine and Oliver, Jason and Olarewaju, Tomiwa and Tesic, J},
+ booktitle={Working Notes Proceedings of the MediaEval 2021 Workshop, Online},
+ pages={13--15},
+ year={2021}
}
-@article{mediaeval00151,
- author = {M Trevisiol and H J{\'e}gou and J Delhumeau and G Gravier},
- title = {Retrieving geo-location of videos with a divide and conquer hierarchical multimodal approach.},
- journal = {ACM International conference on multimedia retrieval},
- year = {2013},
+
+@inproceedings{coutinho2021polyhymnia,
+ title={POLYHYMNIA Mood--Empowering people to cope with depression through music listening},
+ author={Coutinho, Eduardo and Alshukri, Ayesh and de Berardinis, Jacopo and Dowrick, Chris},
+ booktitle={Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
+ pages={188--193},
+ year={2021}
}
-@article{mediaeval00152,
- author = {C Chan and L Lee},
- title = {Model-based unsupervised spoken term detection with spoken queries},
- journal = {IEEE Transactions on Audio, Speech, and Language},
- year = {2013},
+
+@article{meel2023multi,
+ title={Multi-modal fusion using Fine-tuned Self-attention and transfer learning for veracity analysis of web information},
+ author={Meel, Priyanka and Vishwakarma, Dinesh Kumar},
+ journal={Expert Systems with Applications},
+ volume={229},
+ pages={120537},
+ year={2023},
+ publisher={Elsevier}
}
-@article{mediaeval00153,
- author = {LJ Rodr{\'\i}guez-Fuentes and A Varona and M Penagarikano and G Bordel and M Diez},
- title = {GTTS Systems for the SWS Task at MediaEval 2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{constantin2022exploring,
+ title={Exploring deep fusion ensembling for automatic visual interestingness prediction},
+ author={Constantin, Mihai Gabriel and {\c{S}}tefan, Liviu-Daniel and Ionescu, Bogdan},
+ journal={Human Perception of Visual Information: Psychological and Computational Perspectives},
+ pages={33--58},
+ year={2022},
+ publisher={Springer}
}
-@article{mediaeval00154,
- author = {TV Nguyen and MS Dao and R Mattivi and E Sansone and FGB De Natale and G Boato},
- title = {Event Clustering and Classification from Social Media: Watershed-based and Kernel Methods.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{sharma2023ifnd,
+ title={IFND: a benchmark dataset for fake news detection},
+ author={Sharma, Dilip Kumar and Garg, Sonal},
+ journal={Complex \& Intelligent Systems},
+ volume={9},
+ number={3},
+ pages={2843--2863},
+ year={2023},
+ publisher={Springer}
}
-@article{mediaeval00155,
- author = {F Eyben and F Weninger and N Lehment and B Schuller and G Rigoll},
- title = {Affective video retrieval: Violence detection in Hollywood movies by large-scale segmental feature extraction},
- journal = {PloS one},
- year = {2013},
+
+@inproceedings{li2021music,
+ title={Music Emotion Recognition through Sparse Canonical Correlation Analysis},
+ author={Li, Hongwei and Bo, Hongjian and Ma, Lin and Wang, Lexiang and Li, Haifeng},
+ booktitle={2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)},
+ pages={354--359},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00156,
- author = {T Sutanto and R Nayak},
- title = {Admrg@ MediaEval 2013 social event detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{huang2021boosting,
+ title={Boosting advanced nasopharyngeal carcinoma stage prediction using a Two-stage classification framework based on deep learning},
+ author={Huang, Jin and He, Ruhan and Chen, Jia and Li, Song and Deng, Yuqin and Wu, Xinglong},
+ journal={International Journal of Computational Intelligence Systems},
+ volume={14},
+ pages={1--14},
+ year={2021},
+ publisher={Springer}
}
-@article{mediaeval00157,
- author = {N Jain and J Hare and S Samangooei and J Preston and J Davies and D Dupplaw and PH Lewis},
- title = {Experiments in diversifying flickr result sets.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{martin2021sports,
+ title={Sports video: Fine-grained action detection and classification of table tennis strokes from videos for mediaeval 2021},
+ author={Martin, Pierre-Etienne and Calandre, Jordan and Mansencal, Boris and Benois-Pineau, Jenny and P{\'e}teri, Renaud and Mascarilla, Laurent and Morlier, Julien},
+ journal={arXiv preprint arXiv:2112.11384},
+ year={2021}
}
-@article{mediaeval00158,
- author = {Q Dai and J Tu and Z Shi and YG Jiang and X Xue},
- title = {Fudan at MediaEval 2013: Violent Scenes Detection Using Motion Features and Part-Level Attributes.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{tovstogan2021media,
+ title={Media-Eval 2021: Emotion and Theme Recognition in Music Using Jamendo},
+ author={Tovstogan, Philip and Bogdanov, Dmitry and Porter, Alastair},
+ booktitle={Proc. of the MediaEval 2021 Workshop, Online},
+ pages={13--15},
+ year={2021}
}
-@article{mediaeval00159,
- author = {E Acar and F Hopfgartner and S Albayrak},
- title = {Violence detection in hollywood movies by the fusion of visual and mid-level audio cues},
- journal = {ACM international conference on Multimedia},
- year = {2013},
+
+@article{claveau2021generating,
+ title={Generating artificial texts as substitution or complement of training data},
+ author={Claveau, Vincent and Chaffin, Antoine and Kijak, Ewa},
+ journal={arXiv preprint arXiv:2110.13016},
+ year={2021}
}
-@article{mediaeval00160,
- author = {C Guinaudeau and AR Simon and G Gravier and P S{\'e}billot},
- title = {HITS and IRISA at MediaEval 2013: Search and hyperlinking task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{wang2021research,
+ title={Research on the application of wireless wearable sensing devices in interactive music},
+ author={Wang, Huizhong},
+ journal={Journal of Sensors},
+ volume={2021},
+ pages={1--8},
+ year={2021},
+ publisher={Hindawi Limited}
}
-@article{mediaeval00161,
- author = {X Li and M Larson and A Hanjalic},
- title = {Geo-visual ranking for location prediction of social images},
- journal = {ACM International conference on multimedia retrieval},
- year = {2013},
+
+@inproceedings{bhattacharjee2021multimodal,
+ title={Multimodal co-training for fake news identification using attention-aware fusion},
+ author={Bhattacharjee, Sreyasee Das and Yuan, Junsong},
+ booktitle={Asian Conference on Pattern Recognition},
+ pages={282--296},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00162,
- author = {CC Tan and CW Ngo},
- title = {The Vireo Team at MediaEval 2013: Violent Scenes Detection by Mid-level Concepts Learnt from Youtube.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{raj2022arcnn,
+ title={ARCNN framework for multimodal infodemic detection},
+ author={Raj, Chahat and Meel, Priyanka},
+ journal={Neural Networks},
+ volume={146},
+ pages={36--68},
+ year={2022},
+ publisher={Elsevier}
}
-@article{mediaeval00163,
- author = {N Derbas and B Safadi and G Quénot},
- title = {LIG at MediaEval 2013 Affect Task: Use of a Generic Method and Joint Audio-Visual Words.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{xiao2022survey,
+ title={A survey of data representation for multi-modality event detection and evolution},
+ author={Xiao, Kejing and Qian, Zhaopeng and Qin, Biao},
+ journal={Applied Sciences},
+ volume={12},
+ number={4},
+ pages={2204},
+ year={2022},
+ publisher={MDPI}
}
-@article{mediaeval00164,
- author = {S Samangooei and J Hare and D Dupplaw and M Niranjan and N Gibbins and PH Lewis and J Davies and N Jain and J Preston},
- title = {Social event detection via sparse multi-modal feature selection and incremental density based clustering.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{ali2022predicting,
+ title={Predicting Episodic Video Memorability Using Deep Features Fusion Strategy},
+ author={Ali, Hasnain and Gilani, Syed Omer and Khan, Muhammad Jawad and Waris, Asim and Khattak, Muazzam Khan and Jamil, Mohsin},
+ booktitle={2022 IEEE/ACIS 20th International Conference on Software Engineering Research, Management and Applications (SERA)},
+ pages={39--46},
+ year={2022},
+ organization={IEEE}
}
-@article{mediaeval00165,
- author = {MS Dao and G Boato and FGB De Natale and TV Nguyen},
- title = {Jointly exploiting visual and non-visual information for event-related social media retrieval.},
- journal = {ACM International conference on multimedia retrieval},
- year = {2013},
+
+@inproceedings{azri2021monitor,
+ title={MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks},
+ author={Azri, Abderrazek and Favre, C{\'e}cile and Harbi, Nouria and Darmont, J{\'e}r{\^o}me and No{\^u}s, Camille},
+ booktitle={European Conference on Advances in Databases and Information Systems},
+ pages={73--87},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00166,
- author = {D Rafailidis and T Semertzidis and M Lazaridis and MG Strintzis and P Daras},
- title = {A Data-Driven Approach for Social Event Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mahalle2021audio,
+ title={Audio Based Violent Scene Detection Using Extreme Learning Machine Algorithm},
+ author={Mahalle, Mrunali D and Rojatkar, Dinesh V},
+ booktitle={2021 6th International Conference for Convergence in Technology (I2CT)},
+ pages={1--8},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00167,
- author = {CA Bhatt and N Pappas and M Habibi and A Popescu-Belis},
- title = {Idiap at MediaEval 2013: Search and hyperlinking task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{xue2021detecting,
+ title={Detecting fake news by exploring the consistency of multimodal data},
+ author={Xue, Junxiao and Wang, Yabo and Tian, Yichen and Li, Yafei and Shi, Lei and Wei, Lin},
+ journal={Information Processing \& Management},
+ volume={58},
+ number={5},
+ pages={102610},
+ year={2021},
+ publisher={Elsevier}
}
-@article{mediaeval00168,
- author = {Á Erdélyi and T Winkler and B Rinner},
- title = {Serious Fun: Cartooning for Privacy Protection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{aktas2021spatiotemporal,
+ title={Spatiotemporal based table tennis stroke-type assessment},
+ author={Aktas, Kadir and Demirel, Mehmet and Moor, Marilin and Olesk, Johanna and Ozcinar, Cagri and Anbarjafari, Gholamreza},
+ journal={Signal, Image and Video Processing},
+ pages={1--8},
+ year={2021},
+ publisher={Springer}
}
-@article{mediaeval00169,
- author = {M Sjöberg and J Schlater and B Ionescu and M Schedl},
- title = {FAR at MediaEval 2013 Violent Scenes Detection: Concept-based Violent Scenes Detection in Movies.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{yue2020insights,
+ title={Insights of Feature Fusion for Video Memorability Prediction},
+ author={Yue, Fumei and Li, Jing and Sun, Jiande},
+ booktitle={International Forum on Digital TV and Wireless Multimedia Communications},
+ pages={239--248},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00170,
- author = {M Wistuba and L Schmidt-Thieme},
- title = {Supervised Clustering of Social Media Streams.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{zhang2020synchronous,
+ title={Synchronous Prediction of Continuous Affective Video Content Based on Multi-task Learning},
+ author={Zhang, Mingda and Zhong, Wei and Ye, Long and Fang, Li and Zhang, Qin},
+ booktitle={International Forum on Digital TV and Wireless Multimedia Communications},
+ pages={227--238},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00171,
- author = {M Zeppelzauer and M Zaharieva and M Del Fabro},
- title = {Unsupervised Clustering of Social Events.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{dharmapriya2021music,
+ title={Music Emotion Visualization through Colour},
+ author={Dharmapriya, Januka and Dayarathne, Lahiru and Diasena, Tikiri and Arunathilake, Shiromi and Kodikara, Nihal and Wijesekera, Primal},
+ booktitle={2021 International Conference on Electronics, Information, and Communication (ICEIC)},
+ pages={1--6},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00172,
- author = {S Papadopoulos and E Schinas and V Mezaris and R Troncy and I Kompatsiaris},
- title = {The 2012 social event detection dataset.},
- journal = {ACM Multimedia Systems Conference},
- year = {2013},
+
+@article{shu2021v,
+ title={V-SVR+: Support Vector Regression with Variational Privileged Information},
+ author={Shu, Yangyang and Li, Qian and Xu, Chang and Liu, Shaowu and Xu, Guandong},
+ journal={IEEE Transactions on Multimedia},
+ year={2021},
+ publisher={IEEE}
}
-@article{mediaeval00173,
- author = {I. Mironică and B Ionescu and P Knees and P Lambert},
- title = {An in-depth evaluation of multimodal video genre categorization.},
- journal = {IEEE International Workshop on Content-Based Multimedia Indexing (CBMI)},
- year = {2013},
+
+@article{constantin2021visual,
+ title={Visual Interestingness Prediction: A Benchmark Framework and Literature Review},
+ author={Constantin, Mihai Gabriel and {\c{S}}tefan, Liviu-Daniel and Ionescu, Bogdan and Duong, Ngoc QK and Demarty, Claire-Helene and Sj{\"o}berg, Mats},
+ journal={International Journal of Computer Vision},
+ pages={1--25},
+ year={2021},
+ publisher={Springer}
}
-@article{mediaeval00174,
- author = {D Corney and CJ Mart{\'\i}n and A G{\"o}ker and ES Xioufis and S Papadopoulos and Y Kompatsiaris and LM Aiello and B Thomee},
- title = {SocialSensor: Finding Diverse Images at MediaEval 2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{jha2021comprehensive,
+ title={A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging},
+ author={Jha, Debesh and Ali, Sharib and Hicks, Steven and Thambawita, Vajira and Borgli, Hanna and Smedsrud, Pia H and de Lange, Thomas and Pogorelov, Konstantin and Wang, Xiaowei and Harzig, Philipp and others},
+ journal={Medical image analysis},
+ volume={70},
+ pages={102007},
+ year={2021},
+ publisher={Elsevier}
}
-@article{mediaeval00175,
- author = {I Söke and L Burget and F Gr{\'e}zl and L Ondel},
- title = {BUT SWS 2013-Massive Parallel Approach.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@phdthesis{khadkameta,
+ title={Meta-Learning for Medical Image Segmentation},
+ author={Khadka, Rabindra},
+ school={University OF Trieste},
+ year={2021}
}
-@article{mediaeval00176,
- author = {C Penet and CH Demarty and G Gravier and P Gros},
- title = {Technicolor/inria team at the mediaeval 2013 violent scenes detection task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{somandepalli2021computational,
+ title={Computational media intelligence: human-centered machine analysis of media},
+ author={Somandepalli, Krishna and Guha, Tanaya and Martinez, Victor R and Kumar, Naveen and Adam, Hartwig and Narayanan, Shrikanth},
+ journal={Proceedings of the IEEE},
+ year={2021},
+ publisher={IEEE}
}
-@article{mediaeval00177,
- author = {E Acar and F Hopfgartner and S Albayrak},
- title = {Detecting violent content in Hollywood movies by mid-level audio representations},
- journal = {IEEE International Workshop on Content-Based Multimedia Indexing (CBMI)},
- year = {2013},
+
+@phdthesis{martin2020fine,
+ title={Fine-grained action detection and classification from videos with spatio-temporal convolutional neural networks: Application to Table Tennis.},
+ author={Martin, Pierre-Etienne},
+ year={2020},
+ school={Universit{\'e} de Bordeaux}
}
-@article{mediaeval00178,
- author = {M Brenner and E Izquierdo},
- title = {MediaEval 2013: Social Event Detection, Retrieval and Classification in Collaborative Photo Collections.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{tomar2021ddanet,
+ title={DDANet: Dual decoder attention network for automatic polyp segmentation},
+ author={Tomar, Nikhil Kumar and Jha, Debesh and Ali, Sharib and Johansen, H{\aa}vard D and Johansen, Dag and Riegler, Michael A and Halvorsen, P{\aa}l},
+ booktitle={International Conference on Pattern Recognition},
+ pages={307--314},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00179,
- author = {J Davies and J Hare and S Samangooei and J Preston and N Jain and D Dupplaw and PH Lewis},
- title = {Identifying the geographic location of an image with a multimodal probability density function},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{kalakota2021diversifying,
+ title={Diversifying Relevant Search Results from Social Media using Community Contributed Images},
+ author={Kalakota, Vaibhav and Bansal, Ajay},
+ booktitle={2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)},
+ pages={376--385},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00180,
- author = {H Wang and T Lee},
- title = {The CUHK Spoken Web Search System for MediaEval 2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{thao2021attendaffectnet,
+ title={AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies},
+ author={Thao, Ha Thi Phuong and Balamurali, BT and Herremans, Dorien and Roig, Gemma},
+ booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+ pages={8719--8726},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00181,
- author = {J Preston and J Hare and S Samangooei and J Davies and N Jain and D Dupplaw and PH Lewis},
- title = {A unified, modular and multimodal approach to search and hyperlinking video.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{hu2021acoustic,
+ title={Acoustic span embeddings for multilingual query-by-example search},
+ author={Hu, Yushi and Settle, Shane and Livescu, Karen},
+ booktitle={2021 IEEE Spoken Language Technology Workshop (SLT)},
+ pages={935--942},
+ year={2021},
+ organization={IEEE}
}
-@article{mediaeval00182,
- author = {X Anguera and M Skácel and V Vorwerk and J Luque},
- title = {The Telefonica Research Spoken Web Search System for MediaEval 2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{constantin2021deepfusion,
+ title={DeepFusion: Deep Ensembles for Domain Independent System Fusion},
+ author={Constantin, Mihai Gabriel and {\c{S}}tefan, Liviu-Daniel and Ionescu, Bogdan},
+ booktitle={International Conference on Multimedia Modeling},
+ pages={240--252},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00183,
- author = {T De Nies and W De Neve and E Mannens and R Van de Walle},
- title = {Ghent University-iMinds at MediaEval 2013: An Unsupervised Named Entity-based Similarity Measure for Search and Hyperlinking.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@article{lopez2020deep,
+ title={Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data},
+ author={Lopez-Fuentes, Laura and Farasin, Alessandro and Zaffaroni, Mirko and Skinnemoen, Harald and Garza, Paolo},
+ journal={Applied Sciences},
+ volume={10},
+ number={24},
+ pages={8783},
+ year={2020},
+ publisher={Multidisciplinary Digital Publishing Institute}
}
-@article{mediaeval00184,
- author = {D Manchon Vizuete and X Gir{\'o} Nieto},
- title = {Upc at mediaeval 2013 social event detection task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{jain2020tri,
+ title={Tri-Band Assessment of Multi-Spectral Satellite Data for Flood Detection.},
+ author={Jain, Pallavi and Schoen-Phelan, Bianca and Ross, Robert},
+ booktitle={MACLEAN@ PKDD/ECML},
+ year={2020}
}
-@article{mediaeval00185,
- author = {M Riegler and M Lux and C Kofler},
- title = {Frame the Crowd: Global Visual Features Labeling boosted with Crowdsourcing Information.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{duong2020multi,
+ title={Multi-source Machine Learning for AQI Estimation},
+ author={Duong, Dat Q and Le, Quang M and Nguyen-Tai, Tan-Loc and Bo, Dong and Nguyen, Dat and Dao, Minh-Son and Nguyen, Binh T},
+ booktitle={2020 IEEE International Conference on Big Data (Big Data)},
+ pages={4567--4576},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00186,
- author = {A Bursuc and TB Zaharia},
- title = {ARTEMIS@ MediaEval 2013: A Content-Based Image Clustering Method for Public Image Repositories.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{xue2020mvfnn,
+ title={MVFNN: Multi-Vision Fusion Neural Network for Fake News Picture Detection},
+ author={Xue, Junxiao and Wang, Yabo and Xu, Shuning and Shi, Lei and Wei, Lin and Song, Huawei},
+ booktitle={International Conference on Computer Animation and Social Agents},
+ pages={112--119},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00187,
- author = {A Buzo and H Cucu and I Molnar and B Ionescu and C Burileanu},
- title = {SpeeD@ MediaEval 2013: A Phone Recognition Approach to Spoken Term Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{weber2020detecting,
+ title={Detecting natural disasters, damage, and incidents in the wild},
+ author={Weber, Ethan and Marzo, Nuria and Papadopoulos, Dim P and Biswas, Aritro and Lapedriza, Agata and Ofli, Ferda and Imran, Muhammad and Torralba, Antonio},
+ booktitle={European Conference on Computer Vision},
+ pages={331--350},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00188,
- author = {J Vavrek and M Pleva and M Lojka and P Viszlay and E Kiktov{\'a} and D Hl{\'a}dek and J Juh{\'a}r and M Pleva and E Kiktova and D Hladek and J Jozef},
- title = {TUKE at MediaEval 2013 Spoken Web Search Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@phdthesis{duong2020contributions,
+ title={Contributions in Audio Modeling and Multimodal Data Analysis via Machine Learning},
+ author={Duong, Ngoc},
+ year={2020},
+ school={Universit{\'e} de Rennes 1}
}
-@article{mediaeval00189,
- author = {SD Werner and NG Ward},
- title = {Evaluating Prosody-Based Similarity Models for Information Retrieval.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{thao2020deep,
+ title={Deep Neural Networks for Predicting Affective Responses from Movies},
+ author={Thao, Ha Thi Phuong},
+ booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
+ pages={4743--4747},
+ year={2020}
}
-@article{mediaeval00190,
- author = {J Cao},
- title = {Photo Set Refinement and Tag Segmentation in Georeferencing Flickr Photos.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{jony2020fusing,
+ title={Fusing Visual Features and Metadata to Detect Flooding in Flickr Images},
+ author={Jony, Rabiul Islam and Woodley, Alan and Perrin, Dimitri},
+ booktitle={2020 Digital Image Computing: Techniques and Applications (DICTA)},
+ pages={1--8},
+ organization={IEEE}
}
-@article{mediaeval00191,
- author = {B Vandersmissen and A Tomar and F Godin and W De Neve and R Van de Walle},
- title = {Ghent University-iMinds at MediaEval 2013 Diverse Images: Relevance-Based Hierarchical Clustering.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p1,
+ title={Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation},
+ author={Debesh Jha, Steven Hicks, Krister Emanuelsen, Håvard Johansen, Dag Johansen, Thomas de Lange, Michael A. Riegler, Pål Halvorsen},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00192,
- author = {A Popescu},
- title = {CEA LIST's Participation at the MediaEval 2013 Retrieving Diverse Social Images Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p2,
+ title={Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation},
+ author={Adrian Krenzer, Frank Puppe},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00193,
- author = {I Serrano and O D{\'e}niz and GB Garc{\'\i}a},
- title = {VISILAB at MediaEval 2013: Fight Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p3,
+ title={Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus},
+ author={Vajira Thambawita, Steven Hicks, Pål Halvorsen, Michael A. Riegler},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00194,
- author = {F Garc{\'\i}a and E Sanchis and M Calvo and F Pla and LF Hurtado},
- title = {ELiRF at MediaEval 2013: Similar Segments in Social Speech Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p4,
+ title={Generative Adversarial Networks for Automatic Polyp Segmentation},
+ author={Awadelrahman Mohamedelsadig Ali Ahmed},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00195,
- author = {GA Levow},
- title = {UWCL at MediaEval 2013: Similar Segments in Social Speech Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p5,
+ title={Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention},
+ author={Rabindra Khadka},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00196,
- author = {LT Li and J Almeida and OAB Penatti and RT Calumby and DCG Pedronette and MA Gon{\c{c}}alves and RdS Torres},
- title = {Multimodal image geocoding: The 2013 RECOD's approach.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p6,
+ title={HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and U-Net for Polyps Segmentation},
+ author={Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh, Minh-Triet Tran},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00197,
- author = {C Kuoman and S Tollari and M Detyniecki},
- title = {UPMC at MediaEval 2013: Relevance by Text and Diversity by Visual Clustering.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p7,
+ title={A Temporal-Spatial Attention Model for Medical Image Detection},
+ author={Hwang Maxwell, Wu Cai, Hwang Kao-Shing, Xu Yong Si, Wu Chien-Hsing},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00198,
- author = {JA Gómez and LF Hurtado and M Calvo and E Sanchis},
- title = {ELiRF at MediaEval 2013: Spoken Web Search Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p8,
+ title={Deep Conditional Adversarial Learning for Polyp Segmentation},
+ author={Debapriya Banik , Debotosh Bhattacharjee},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00199,
- author = {GV Mantena and K Prahallad},
- title = {IIIT-H SWS 2013: Gaussian Posteriorgrams of Bottle-Neck Features for Query-by-Example Spoken Term Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p9,
+ title={Automatic Polyp Segmentation Using Channel-Spatial Attention with Deep Supervision},
+ author={Sahadev Poudel, Sang-Woong Lee},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00200,
- author = {M Eskevich and GJF Jones},
- title = {Time-based segmentation and use of jump-in points in DCU search runs at the Search and Hyperlinking task at MediaEval 2013},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p10,
+ title={Automatic Polyp Segmentation Using Fully Convolutional Neural Network},
+ author={Nikhil Kumar Tomar},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00201,
- author = {K Aoyama and A Ogawa and T Hattori and T Hori and A Nakamura},
- title = {Graph index based query-by-example search on a large speech data set},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2013},
+
+@inproceedings{mediaeval2020p11,
+ title={Automatic Polyp Segmentation via Parallel Reverse Attention Network},
+ author={Ge-Peng Ji, Deng-Ping Fan, Tao Zhou, Geng Chen, Huazhu Fu, Ling Shao},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00202,
- author = {P Galuščáková and P Pecina},
- title = {CUNI at MediaEval 2013 Similar Segments in Social Speech Task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p12,
+ title={Real-Time Polyp Segmentation Using U-Net with IoU Loss},
+ author={George Batchkala, Sharib Ali},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00203,
- author = {A Ali and M Clements},
- title = {Spoken Web Search using an Ergodic Hidden Markov Model of Speech.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p13,
+ title={Depth-Wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Intestinal Tract},
+ author={Syed Muhammad Faraz Ali, Muhammad Taha Khan, Syed Unaiz Haider, Talha Ahmed, Zeshan Khan, Muhammad Atif Tahi},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00204,
- author = {M Bouallegue and G Senay and M Morchid and D Matrouf and G Linar{\`e}s and R Dufour},
- title = {LIA@ MediaEval 2013 Spoken Web Search Task: An I-Vector based Approach.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p14,
+ title={HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation},
+ author={Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen, Minh-Triet Tran},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00205,
- author = {P Galuščáková and P Pecina},
- title = {CUNI at MediaEval 2012 Search and Hyperlinking Task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p15,
+ title={Ensemble U-Net Model for Efficient Polyp Segmentation},
+ author={Shruti Shrestha, Bishesh Khanal, Sharib Ali},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00206,
- author = {C Ventura and M Tella-Amo and XG i Nieto},
- title = {UPC at MediaEval 2013 Hyperlinking Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p16,
+ title={Efficient Supervision Net: Polyp Segmentation Using EfficientNet and Attention Unit},
+ author={Sabari Nathan, Suganya Ramamoorthy},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00207,
- author = {M Morchid and R Dufour and G Linares},
- title = {Event detection from image hosting services by slightly-supervised multi-span context models},
- journal = {International Workshop on Content-Based Multimedia Indexing (CBMI)},
- year = {2013},
+
+@inproceedings{mediaeval2020p17,
+ title={KD-ResUNet++: Automatic Polyp Segmentation via Self-Knowledge Distillation},
+ author={Jaeyong Kang, Jeonghwan Gwak},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00208,
- author = {M Lokaj and H Stiegler and W Bailer},
- title = {TOSCA-MP at Search and Hyperlinking of Television Content Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p18,
+ title={Automatic Polyp Segmentation Using U-Net-ResNet50},
+ author={Saruar Alam, Nikhil Kumar Tomar, Aarati Thakur, Debesh Jha, Ashish Rauniyar},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00209,
- author = {K Schouten and R Aly and R Ordelman},
- title = {Searching and Hyperlinking using Word Importance Segment Boundaries in MediaEval 2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p19,
+ title={FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020},
+ author={Konstantin Pogorelov, Daniel Thilo Schroeder, Luk Burchard, Johannes Moe, Stefan Brenner, Petra Filkukova, Johannes Langguth},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00210,
- author = {P Knees and M Schedl},
- title = {Music similarity and retrieval},
- journal = {Proceedings of the international ACM SIGIR},
- year = {2013},
+
+@inproceedings{mediaeval2020p20,
+ title={Detecting Conspiracy Tweets Using Support Vector Machines},
+ author={Manfred Moosleitner, Benjamin Murauer, Günther Specht},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00211,
- author = {A Armagan and A Popescu and P Duygulu},
- title = {Mucke participation at retrieving diverse social images task of mediaeval 2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p21,
+ title={MediaEval 2020: An Ensemble-based Multimodal Approach for Coronavirus and 5G Conspiracy Tweet Detection},
+ author={Chahat Raj, Mihir Mehta},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00212,
- author = {S Goto and T Aoki},
- title = {TUDCL at MediaEval 2013 Violent Scenes Detection: Training with Multi-modal Features by MKL.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p22,
+ title={Fake News Classification with BERT},
+ author={Andrey Malakhov, Alessandro Patruno, Stefano Bocconi},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00213,
- author = {V Lam and S Phan and TD Ngo and DD Le and DA Duong and S Satoh},
- title = {Violent scene detection using mid-level feature.},
- journal = {Proceedings of the Fourth Symposium on Information and Communication Technology},
- year = {2013},
+
+@inproceedings{mediaeval2020p23,
+ title={FakeNews Detection Using Pre-trained Language Models and Graph Convolutional Networks},
+ author={Manh Duc Tuan Nguyen, Minh Quang Nhat Pham},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00214,
- author = {A Sheshadri and M Lease},
- title = {SQUARE: Benchmarking Crowd Consensus at MediaEval.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p24,
+ title={Fake News Detection in Social Media Using Graph Neural Networks and NLP Techniques: A COVID-19 Use-Case},
+ author={Abdullah Hamid, Nasrullah Sheikh, Naina Said, Kashif Ahmad, Asma Gul, Laiq Hasan, Ala Al-Fuqaha},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00215,
- author = {J Hare and M Acosta and A Weston and E Simperl and S Samangooei and D Dupplaw and PH Lewis},
- title = {An investigation of techniques that aim to improve the quality of labels provided by the crowd.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p25,
+ title={TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy},
+ author={Gullal Singh Cheema, Sherzod Hakimov, Ralph Ewerth},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00216,
- author = {A Melle and JL Dugelay},
- title = {Shape and Color-aware Privacy Protection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p26,
+ title={You Said It? How Mis- and Disinformation Tweets Surrounding the Corona-5G-Conspiracy Communicate Through Implying},
+ author={Lynn de Rijk},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00217,
- author = {HJ Escalante and A Morales-Reyes},
- title = {TIA-INAOE's Approach for the 2013 Retrieving Diverse Social Images Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p27,
+ title={Using a Word Analysis Method and GNNs to Classify Misinformation Related to 5G-Conspiracy and the COVID-19 Pandemic},
+ author={Ferdinand Schaal, Jesper Phillips},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00218,
- author = {AL Radu and B Boteanu and O Ples and B Ionescu},
- title = {LAPI@ Retrieving Diverse Social Images Task 2013: Qualitative Photo Retrieval using Multimedia Content.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p28,
+ title={Detecting Fake News in Tweets from Text and Propagation Graph: IRISA's Paritcipation to the FakeNews Task at MediaEval 2020},
+ author={Vincent Claveau},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00219,
- author = {R Jarina and M Kuba and R Gubka and M Chmulik and M Paralic},
- title = {UNIZA System for the Spoken Web Search Task at MediaEval2013.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p29,
+ title={Evaluating Standard Classifiers for Detecting COVID-19 Related Misinformation},
+ author={Daniel Thilo Schroeder, Konstantin Pogorelov, Johannes Langguth},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00220,
- author = {S Magliacane and PT Groth},
- title = {Repurposing Benchmark Corpora for Reconstructing Provenance.},
- journal = {SePublica},
- year = {2013},
+
+@inproceedings{mediaeval2020p30,
+ title={Enriching Content Analysis of Tweets Using Community Discovery Graph Analysis},
+ author={Andrew Magill, Lia Nogueira de Moura, Maria Tomasso, Mirna Elizondo, Jelena Tesic},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00221,
- author = {M Georgescu and X Zhu},
- title = {L3S at MediaEval 2013 Crowdsourcing for Social Multimedia Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p31,
+ title={Detecting Conspiracy Theories from Tweets: Textual and Structural Approaches},
+ author={Haoming Guo, Adam Ash, David Chung, Gerald Friedland},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00222,
- author = {D Maniry and E Acar and S Albayrak},
- title = {DAI at the MediaEval 2013 Visual Privacy Task: Representing People with Foreground Edges.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p32,
+ title={On the pursuit of Fake News : From Graph Convolutional Networks to Time Series},
+ author={Zeynep Pehlivan},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00223,
- author = {V Lam and DD Le and S Phan and S Satoh and DA Duong and TD Ngo},
- title = {Evaluation of Low-Level features for detecting Violent Scenes in videos},
- journal = {International Conference on Soft Computing and Pattern Recognition (SoCPaR)},
- year = {2013},
+
+@inproceedings{mediaeval2020p33,
+ title={MeVer Team Tackling Corona Virus and 5G Conspiracy Using Ensemble Classification Based on BERT},
+ author={Olga Papadopoulou, Giorgos Kordopatis-Zilos, Symeon Papadopoulos},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00224,
- author = {I Tankoyeu and J Stöttinger and F Giunchiglia},
- title = {Context-based media geotagging of personal photos},
- journal = {Technical report},
- year = {2013},
+
+@inproceedings{mediaeval2020p34,
+ title={Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?},
+ author={Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin, Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu, Alan F. Smeaton},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00225,
- author = {M Brenner and E Izquierdo},
- title = {Event-driven retrieval in collaborative photo collections},
- journal = {International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)},
- year = {2013},
+
+@inproceedings{mediaeval2020p35,
+ title={Media Memorability Prediction Based on Machine Learning},
+ author={Dazhan Xu, Xiaoyu Wu, Guoquan Sun},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00226,
- author = {M Morchid and R Dufour and M Bouallegue and G Linarès and D Matrouf},
- title = {LIA@ MediaEval 2013 MusiClef Task: A Combined Thematic and Acoustic Approach.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p36,
+ title={Investigating Memorability of Dynamic Media},
+ author={Phuc H. Le-Khac, Ayush K. Rai, Graham Healy, Alan F. Smeaton, Noel E. O’connor},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00227,
- author = {M Morchid and R Dufour and M Bouallegue and G Linarès and D Matrouf},
- title = {LIA@ MediaEval 2013 Crowdsourcing Task: Metadata or not Metadata? That is a Fashion Question.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p37,
+ title={Leveraging Audio Gestalt to Predict Media Memorability},
+ author={Lorin Sweeney, Graham Healy, Alan F. Smeaton},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00228,
- author = {F Garc{\'\i}a Granada and E Sanch{\'\i}s Arnal and M Calvo Lance and F Pla Santamar{\'\i}a and LF Hurtado Oliver},
- title = {ELIRF at MEDIAEVAL 2013: Similar Segments of Social Speech Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p38,
+ title={Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task},
+ author={Janadhip Jacutprakart, Rukiye Savran Kiziltepe, John Q. Gan, Giorgos Papanastasiou, Alba G. Seco de Herrera},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00229,
- author = {MH Sedky and CC Chibelushi and M Moniri},
- title = {MediaEval 2013 Visual Privacy Task: Physics-Based Technique for Protecting Privacy in Surveillance Videos.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p39,
+ title={Multi-Modal Ensemble Models for Predicting Video Memorability},
+ author={Tony Zhao, Irving Fang, Jeffrey Kim, Gerald Friedland},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00230,
- author = {M Sedky and CC Chibelushi and M Moniri},
- title = {Physics-Based Technique for Protecting Privacy in Surveillance Videos},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p40,
+ title={Predicting Media Memorability with Audio, Video, and Text representations},
+ author={Alison Reboud, Ismail Harrando, Jorma Laaksonen, Raphaël Troncy},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00231,
- author = {S Schmiedeke and P Kelm and T Sikora},
- title = {TUB@ MediaEval 2013 Visual Privacy Task: Reversible Scrambling with colour-preservative Characteristic.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p41,
+ title={Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention and LSTM Models},
+ author={Ricardo Kleinlein, Cristina Luna-Jiménez, Zoraida Callejas, Fernando Fernández-Martínez},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00232,
- author = {B do Nascimento Teixeira},
- title = {MTM at MediaEval 2013 Violent Scenes Detection: Through Acoustic-visual Transform.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p42,
+ title={MediaEval 2020: Emotion and Theme Recognition in Music Using Jamendo},
+ author={Dmitry Bogdanov, Alastair Porter, Philip Tovstogan, Minz Won},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00233,
- author = {C Gracia and X Anguera and X Binefa},
- title = {The CMTECH Spoken Web Search System for MediaEval},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p43,
+ title={Recognizing Song Mood and Theme: Leveraging Ensembles of Tag Groups},
+ author={Michael Vötter, Maximilian Mayerl, Günther Specht, Eva Zangerle},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00234,
- author = {C Wartena},
- title = {Segmentation Strategies for Passage Retrieval from Internet Video using Speech Transcripts},
- journal = {Journal of Digital Information Management},
- year = {2013},
+
+@inproceedings{mediaeval2020p44,
+ title={Emotion and Theme Recognition in Music Using Attention-Based Methods},
+ author={Srividya Tirunellai Rajamani, Kumar Rajamani, Björn Schuller},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00235,
- author = {C Pantoja and VF Arguedas and E Izquierdo},
- title = {MediaEval 2013 Visual Privacy Task: Pixel Based Anonymisation Technique.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013},
+
+@inproceedings{mediaeval2020p45,
+ title={HCMUS at MediaEval 2020: Emotion Classification Using Wavenet Feature with SpecAugment and EfficientNet},
+ author={Tri-Nhan Do, Minh-Tri Nguyen, Hai-Dang Nguyen, Minh-Triet Tran, Xuan-Nam Cao},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00028,
- author = {K Yanai and DH Nga},
- title = {UEC, Tokyo at MediaEval 2013 Retrieving Diverse Social Images Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013}
+
+@inproceedings{mediaeval2020p46,
+ title={Emotion and Themes Recognition in Music with Convolutional and Recurrent Attention-Blocks},
+ author={Maurice Gerczuk, Shahin Amiriparian, Sandra Ottl, Srividya Tirunellai Rajamani, Björn Schuller},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00048,
- author = {K Tserpes and M Kardara and T Varvarigou},
- title = {A similarity-based Chinese Restaurant Process for Social Event Detection},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013}
+
+@inproceedings{mediaeval2020p47,
+ title={MediaEval 2020 Emotion and Theme Recognition in Music Task: Loss Function Approaches for Multi-label Music Tagging},
+ author={Dillon Knox, Timothy Greer, Benjamin Ma, Emily Kuo, Krishna Somandepalli, Shrikanth Narayanan},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00049,
- author = {JLR Garcia and R Troncy and L Pikora},
- title = {LinkedTV at MediaEval 2013 Search and Hyperlinking Task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2013}
+
+@inproceedings{mediaeval2020p48,
+ title={Recognizing Music Mood and Theme Using Convolutional Neural Networks and Attention},
+ author={Alish Dipani, Gaurav Iyer, Veeky Baths},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00236,
- author = {M Ruocco and H Ramampiaro},
- title = {Event-related image retrieval: exploring geographical and temporal distribution of user tags},
- journal = {International Journal of Multimedia Information Retrieval},
- year = {2013},
+
+@inproceedings{mediaeval2020p49,
+ title={Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020},
+ author={Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Laurent Mascarilla, Jordan Calandre, Julien Morlier},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00237,
- author = {E Apostolidis and V Mezaris and CD Stein and S Eickeler},
- title = {Television Linked To The Web},
- year = {2013},
+
+@inproceedings{mediaeval2020p50,
+ title={Leveraging Human Pose Estimation Model for Stroke Classification in Table Tennis},
+ author={Soichiro Sato, Masaki Aono},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00238,
- author = {M Eskevich and R Aly and D Racca and R Ordelman and S Chen and GJF Jones},
- title = {The search and hyperlinking task at MediaEval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p51,
+ title={Four-Stream Network and Dynamic Images for Sports Video Classification: Classification of Strokes in Table Tennis},
+ author={Jordan Calandre, Renaud Péteri, Laurent Mascarilla},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00239,
- author = {B Ionescu and AL Gansca and B Boteanu and A Popescu and M Lupu and H Müller},
- title = {Retrieving Diverse Social Images at MediaEval 2014: Challenge, Dataset and Evaluation.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p52,
+ title={HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task},
+ author={Hai Nguyen-Truong, San Cao, N. A. Khoa Nguyen, Bang-Dang Pham, Hieu Dao, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen, Minh-Triet Tran},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00240,
- author = {LJ Rodriguez-Fuentes and A Varona and M Penagarikano and G Bordel and M Diez},
- title = {High-performance query-by-example spoken term detection on the SWS 2013 evaluation.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2014},
+
+@inproceedings{mediaeval2020p53,
+ title={Spatio-Temporal Based Table Tennis Hit Assessment Using LSTM Algorithm},
+ author={Kadir Aktas, Mehmet Demirel, Marilin Moor, Johanna Olesk, Gholamreza Anbarjafari},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00241,
- author = {X Anguera and LJ Rodriguez-Fuentes and I Söke and A Buzo and F Metze},
- title = {Query by Example Search on Speech at Mediaeval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p54,
+ title={Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020},
+ author={Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Julien Morlier},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00242,
- author = {F Metze and X Anguera and E Barnard and M Davel and G Gravier},
- title = {Language independent search in MediaEval's Spoken Web Search task.},
- journal = {Computer Speech and Language},
- year = {2014},
+
+@inproceedings{mediaeval2020p55,
+ title={The Flood-Related Multimedia Task at MediaEval 2020},
+ author={Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris, Roberto Fiorin, Daniele Norbiato, Michele Ferri},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00243,
- author = {B Safadi and M Sahuguet and B Huet},
- title = {When textual and visual information join forces for multimedia retrieval},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2014},
+
+@inproceedings{mediaeval2020p56,
+ title={Flood Detection in Twitter Using a Novel Learning Method for Neural Networks},
+ author={Rabiul Islam Jony, Alan Woodley},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00244,
- author = {I Söke and L B{\"u}rget and F Gr{\'e}zl and JH {\v{C}}ernocky and L Ondel},
- title = {Calibration and fusion of query-by-example systems - BUT SWS 2013.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2014},
+
+@inproceedings{mediaeval2020p57,
+ title={A Tweet Text Binary Artificial Neural Network Classifier},
+ author={Theodore Nikoletopoulos, Claudia Wolff},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00245,
- author = {G Petkos and S Papadopoulos and V Mezaris and Y Kompatsiaris},
- title = {Social Event Detection at MediaEval 2014: Challenges, Datasets, and Evaluation.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p58,
+ title={Floods Detection in Twitter Text and Images},
+ author={Naina Said, Kashif Ahmad, Asma Gul, Nasir Ahmad, Ala Al-Fuqaha},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00246,
- author = {X Anguera and LJ Rodriguez-Fuentes and I Söke and A Buzo and F Metze and M Penagarikano},
- title = {Query-by-example spoken term detection evaluation on low-resource languages.},
- journal = {Spoken Language Technologies for Under-Resourced Languages},
- year = {2014},
+
+@inproceedings{mediaeval2020p59,
+ title={Flood Detection via Twitter Streams Using Textual and Visual Features},
+ author={Firoj Alam, Zohaib Hassan, Kashif Ahmad, Asma Gul, Michael Reiglar, Nicola Conci, Ala Al-Fuqaha},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00247,
- author = {I Söke and M Skácel and L Burget},
- title = {BUT QUESST 2014 system description.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p60,
+ title={Ensemble Based Method for the Classification of Flooding Event Using Social Media Data},
+ author={Muhammad Hanif, Huzaifa Joozer, Muhammad Atif Tahir, Muhammad Rafi},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00248,
- author = {DT Dang-Nguyen and L Piras and G Giacinto and G Boato and FGB De Natale},
- title = {Retrieval of Diverse Images by Pre-filtering and Hierarchical Clustering.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p61,
+ title={Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis},
+ author={Peijiang Zhao, Minh-Son Dao, Thanh Nguyen, Thanh-Binh Nguyen, Duc-Tien Dang-Nguyen, Cathal Gurrin},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00249,
- author = {G Petkos and S Papadopoulos and E Schinas and Y Kompatsiaris},
- title = {Graph-based multimodal clustering for social event detection in large collections of images.},
- journal = {International Conference on Multimedia Modeling},
- year = {2014},
+
+@inproceedings{mediaeval2020p62,
+ title={A2QI: An Approach for Air Pollution Estimation in MediaEval 2020},
+ author={Dat Q. Duong, Quang M. Le, Dat T. Nguyen},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00250,
- author = {E Apostolidis and V Mezaris and M Sahuguet and B Huet and B {\v{C}}ervenkov{\'a} and D Stein and S Eickeler and JL Redondo Garcia and R Troncy and L Pikora},
- title = {Automatic fine-grained hyperlinking of videos within a closed collection using scene segmentation.},
- journal = {ACM international conference on Multimedia},
- year = {2014},
+
+@inproceedings{mediaeval2020p63,
+ title={Personal Air Quality Index Prediction Using Inverse Distance Weighting Method},
+ author={Trung-Quan Nguyen, Dang-Hieu Nguyen, Loc Tai Tan Nguyen},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00251,
- author = {E Coutinho and F Weninger and BW Schuller and KR Scherer},
- title = {The Munich LSTM-RNN Approach to the MediaEval 2014 Emotion in Music Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p64,
+ title={Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance},
+ author={Trung-Quan Nguyen, Dang-Hieu Nguyen, Loc Tai Tan Nguyen},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00252,
- author = {P Yang and H Xu and X Xiao and L Xie and CC Leung and H Chen and J Yu and H Lv and L Wang and SJ Leow and others},
- title = {The NNI Query-by-Example System for MediaEval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p65,
+ title={Insights for Wellbeing: Predicting Personal Air Quality Index Using Regression Approach},
+ author={Amel Ksibi, Amina Salhi, Ala Alluhaidan, Sahar A. El-Rahman},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00253,
- author = {V Lam and DD Le and S Phan and Shinichi Satoh and DA Duong},
- title = {NII-UIT at MediaEval 2014 Violent Scenes Detection Affect Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p66,
+ title={Pixel Privacy: Quality Camouflage for Social Images},
+ author={Zhuoran Liu, Zhengyu Zhao, Martha Larson, Laurent Amsaleg},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00254,
- author = {Q Dai and Z Wu and YG Jiang and X Xue and J Tang},
- title = {Fudan-NJUST at MediaEval 2014: Violent Scenes Detection Using Deep Neural Networks.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p67,
+ title={Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable Color Filter},
+ author={Zhengyu Zhao},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00255,
- author = {G Mantena and K Prahallad},
- title = {Use of articulatory bottle-neck features for query-by-example spoken term detection in low resource scenarios},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2014},
+
+@inproceedings{mediaeval2020p68,
+ title={MediaEval 2020: Maintaining Human-Imperceptibility of Image Adversarial Attack by Using Human-Aware Sensitivity Map},
+ author={Zhiqi Shen, Muhammad Habibi, Shaojing Fan, Mohan Kankanhalli},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00256,
- author = {N Kumar and R Gupta and T Guha and C Vaz and M Van Segbroeck and J Kim and SS Narayanan},
- title = {Affective Feature Design and Predicting Continuous Affective Dimensions from Music.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p69,
+ title={HCMUS at Pixel Privacy 2020: Quality Camouflage with Back Propagation and Image Enhancement},
+ author={Minh-Khoi Pham, Hai-Tuan Ho-Nguyen, Trong-Thang Pham, Hung Vinh Tran, Hai-Dang Nguyen, Minh-Triet Tran},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00257,
- author = {BE Ionescu and K Seyerlehner and I. Mironică and C Vertan and P Lambert},
- title = {An audio-visual approach to web video categorization.},
- journal = {Multimedia Tools and Applications},
- year = {2014},
+
+@inproceedings{mediaeval2020p70,
+ title={Fooling an Automatic Image Quality Estimator},
+ author={Benoit Bonnet, Teddy Furon, Patrick Bas},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00258,
- author = {U Ahsan and I Essa},
- title = {Clustering social event images using kernel canonical correlation analysis},
- journal = {IEEE Conference on Computer Vision and Pattern Recognition Workshops},
- year = {2014},
+
+@inproceedings{mediaeval2020p71,
+ title={No-Audio Multimodal Speech Detection Task at MediaEval 2020},
+ author={Laura Cabrera-Quiros, Jose Vargas-Quirós, Hayley Hung},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00259,
- author = {S Avila and D Moreira and M Perez and D Moraes and I Cota and V Testoni and E Valle and S Goldenstein and A Rocha and others},
- title = {RECOD at MediaEval 2014: Violent scenes detection task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p72,
+ title={Multimodal Fusion of Body Movement Signals for No-audio Speech Detection},
+ author={Xinsheng Wang, Jihua Zhu, Odette Scharenborg},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00260,
- author = {LT Li and DCG Pedronette and J Almeida and OAB Penatti and RT Calumby and RdaS Torres},
- title = {A rank aggregation framework for video multimodal geocoding.},
- journal = {Multimedia Tools and Applications},
- year = {2014},
+
+@inproceedings{mediaeval2020p73,
+ title={News Images in MediaEval 2020},
+ author={Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00261,
- author = {K Apostolidis and C Papagiannopoulou and V Mezaris},
- title = {CERTH at MediaEval 2014 Synchronization of Multi-User Event Media Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{mediaeval2020p74,
+ title={HCMUS at MediaEval 2020: Image-Text Fusion for Automatic News-Images Re-Matching},
+ author={Thuc Nguyen-Quang, Tuan-Duy Nguyen, Thang-Long Nguyen-Ho, Anh-Kiet Duong, Xuan-Nhat Hoang, Vinh-Thuyen Nguyen-Truong, Hai-Dang Nguyen, Minh-Triet Tran},
+ booktitle={Working Notes Proceedings of the MediaEval 2020 Workshop},
+ year={2020}
}
-@article{mediaeval00262,
- author = {B Zhang and Y Yi and H Wang and J Yu},
- title = {MIC-TJU at MediaEval Violent Scenes Detection (VSD) 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{zhang2020bdann,
+ title={BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection},
+ author={Zhang, Tong and Wang, Di and Chen, Huanhuan and Zeng, Zhiwei and Guo, Wei and Miao, Chunyan and Cui, Lizhen},
+ booktitle={2020 international joint conference on neural networks (IJCNN)},
+ pages={1--8},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00263,
- author = {C Bhatt and N Pappas and M Habibi and A Popescu-Belis},
- title = {Multimodal reranking of content-based recommendations for hyperlinking video snippets.},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2014},
+
+@inproceedings{giachanou2020multimodal,
+ title={Multimodal fake news detection with textual, visual and semantic information},
+ author={Giachanou, Anastasia and Zhang, Guobiao and Rosso, Paolo},
+ booktitle={International Conference on Text, Speech, and Dialogue},
+ pages={30--38},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00264,
- author = {B do Nascimento Teixeira},
- title = {Mtm at mediaeval 2014 violence detection task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{da2021hierarchical,
+ title={Hierarchical multi-label propagation using speaking face graphs for multimodal person discovery},
+ author={da Fonseca, Gabriel Barbosa and Sargent, Gabriel and Sicre, Ronan and Patroc{\'\i}nio, Zenilton KG and Gravier, Guillaume and Guimaraes, Silvio Jamil F},
+ journal={Multimedia Tools and Applications},
+ volume={80},
+ number={2},
+ pages={2797--2820},
+ year={2021},
+ publisher={Springer}
}
-@article{mediaeval00265,
- author = {B Boteanu and I. Mironică and B Ionescu},
- title = {A relevance feedback perspective to image search result diversification},
- journal = {International Conference on Intelligent Computer Communication and Processing (ICCP)},
- year = {2014},
+
+@inproceedings{tanwar2020multi,
+ title={Multi-Model Fake News Detection based on Concatenation of Visual Latent Features},
+ author={Tanwar, Vidhu and Sharma, Kapil},
+ booktitle={2020 International Conference on Communication and Signal Processing (ICCSP)},
+ pages={1344--1348},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00266,
- author = {M Sjöberg and I Mironica and M Schedl and B Ionescu},
- title = {FAR at MediaEval 2014 Violent Scenes Detection: A Concept-based Fusion Approach.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{ou2020multimodal,
+ title={Multimodal local-global attention network for affective video content analysis},
+ author={Ou, Yangjun and Chen, Zhenzhong and Wu, Feng},
+ journal={IEEE Transactions on Circuits and Systems for Video Technology},
+ volume={31},
+ number={5},
+ pages={1901--1914},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00267,
- author = {D Castán and M Rodríguez and A Ortega and C Orrite and E Lleida},
- title = {ViVoLab and CVLab-MediaEval 2014: Violent Scenes Detection Affect Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{rambour2020flood,
+ title={Flood detection in time series of optical and sar images},
+ author={Rambour, Cl{\'e}ment and Audebert, Nicolas and Koeniguer, E and Le Saux, Bertrand and Crucianu, M and Datcu, Mihai},
+ journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
+ volume={43},
+ pages={1343--1346},
+ year={2020}
}
-@article{mediaeval00268,
- author = {N Derbas and G Quénot},
- title = {Joint audio-visual words for violent scenes detection in movies.},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2014},
+
+@article{bouhlel2020hypergraph,
+ title={Hypergraph-based image search reranking with elastic net regularized regression},
+ author={Bouhlel, Noura and Feki, Ghada and Amar, Chokri Ben},
+ journal={Multimedia Tools and Applications},
+ volume={79},
+ number={41},
+ pages={30257--30280},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00269,
- author = {J Proença and A Veiga and F Perdigao},
- title = {The SPL-IT Query by Example Search on Speech system for MediaEval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{astefanoaei2020hyperbolic,
+ title={Hyperbolic Embeddings for Music Taxonomy},
+ author={Astefanoaei, Maria and Collignon, Nicolas},
+ booktitle={Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)},
+ pages={38--42},
+ year={2020}
}
-@article{mediaeval00270,
- author = {RFE Sutcliffe and T Crawford and C Fox and DL Root and E Hovy},
- title = {Shared evaluation of natural language queries against classical music scores: A full description of the C@merata 2014 task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{armitage2020mlm,
+ title={Mlm: A benchmark dataset for multitask learning with multiple languages and modalities},
+ author={Armitage, Jason and Kacupaj, Endri and Tahmasebzadeh, Golsa and Maleshkova, Maria and Ewerth, Ralph and Lehmann, Jens},
+ booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
+ pages={2967--2974},
+ year={2020}
}
-@article{mediaeval00271,
- author = {P Galuščáková and P Pecina},
- title = {Experiments with segmentation strategies for passage retrieval in audio-visual documents},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2014},
+
+@inproceedings{zhao2021musicoder,
+ title={MusiCoder: A Universal Music-Acoustic Encoder Based on Transformer},
+ author={Zhao, Yilun and Guo, Jia},
+ booktitle={International Conference on Multimedia Modeling},
+ pages={417--429},
+ year={2021},
+ organization={Springer}
}
-@article{mediaeval00272,
- author = {T Sutanto and R Nayak},
- title = {Ranking Based Clustering for Social Event Detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{rana2020image,
+ title={Image Based Fake Tweet Retrieval (IBFTR)},
+ author={Rana, Dipti P and Bawkar, Simran and Jain, Mansi and Bothra, Swati and Baldaniya, Shailesh},
+ booktitle={2020 International Conference for Emerging Technology (INCET)},
+ pages={1--8},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00273,
- author = {LJ Rodríguez-Fuentes and A Varona and M Penagarikano and G Bordel and M Diez},
- title = {GTTS-EHU Systems for QUESST at MediaEval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{koutini2020receptive,
+ title={Receptive-field regularized CNNs for music classification and tagging},
+ author={Koutini, Khaled and Eghbal-Zadeh, Hamid and Haunschmid, Verena and Primus, Paul and Chowdhury, Shreyan and Widmer, Gerhard},
+ journal={arXiv preprint arXiv:2007.13503},
+ year={2020}
}
-@article{mediaeval00274,
- author = {S Chen and GJF Jones and NE O'Connor},
- title = {Dcu linking runs at mediaeval 2014: search and hyperlinking task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{gao2020novel,
+ title={A Novel Music Emotion Recognition Model for Scratch-generated Music},
+ author={Gao, Zijing and Qiu, Lichen and Qi, Peng and Sun, Yan},
+ booktitle={2020 International Wireless Communications and Mobile Computing (IWCMC)},
+ pages={1794--1799},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00275,
- author = {B Loni and J Hare and M Georgescu and M Riegler and X Zhu and M Morchid and R Dufour and M Larson},
- title = {Getting by with a little help from the crowd: Practical approaches to social image labeling.},
- journal = {ACM Workshop on Crowdsourcing for Multimedia},
- year = {2014},
+
+@phdthesis{patel2020precise,
+ title={Precise Image Exploration With Cluster Analysis},
+ author={Patel, Sagar},
+ year={2020}
}
-@article{mediaeval00276,
- author = {M Zaharieva and D Schopfhauser and M Del Fabro and M Zeppelzauer},
- title = {Clustering and Retrieval of Social Events in Flickr.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{deekshitha2020multilingual,
+ title={Multilingual spoken term detection: a review},
+ author={Deekshitha, G and Mary, Leena},
+ journal={International Journal of Speech Technology},
+ volume={23},
+ number={3},
+ pages={653--667},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00277,
- author = {E Spyromitros-Xioufis and S Papadopoulos and Y Kompatsiaris and I Vlahavas},
- title = {Socialsensor: Finding diverse images at mediaeval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{fadel2020neural,
+ title={Neural relational inference for disaster multimedia retrieval},
+ author={Fadel, Samuel G and Torres, Ricardo da S},
+ journal={Multimedia Tools and Applications},
+ volume={79},
+ number={35},
+ pages={26735--26746},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00278,
- author = {B Ionescu and A Popescu and H Muller and M Menendez and AL Radu},
- title = {Benchmarking result diversification in social image retrieval.},
- journal = {IEEE International Conference on Image Processing (ICIP)},
- year = {2014},
+
+@article{tahmasebzadeh2020feature,
+ title={A Feature Analysis for Multimodal News Retrieval},
+ author={Tahmasebzadeh, Golsa and Hakimov, Sherzod and M{\"u}ller-Budack, Eric and Ewerth, Ralph},
+ journal={arXiv preprint arXiv:2007.06390},
+ year={2020}
}
-@article{mediaeval00279,
- author = {S Goto and T Aoki},
- title = {Violent scenes detection using mid-level violence clustering},
- journal = {Computer Science CSCP},
- year = {2014},
+
+@inproceedings{gammulle2020two,
+ title={Two-stream deep feature modelling for automated video endoscopy data analysis},
+ author={Gammulle, Harshala and Denman, Simon and Sridharan, Sridha and Fookes, Clinton},
+ booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
+ pages={742--751},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00280,
- author = {Y Fan and M Xu},
- title = {MediaEval 2014: THU-HCSIL Approach to Emotion in Music Task using Multi-level Regression.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{pereira2020assessing,
+ title={Assessing flood severity from crowdsourced social media photos with deep neural networks},
+ author={Pereira, Jorge and Monteiro, Joao and Silva, Joel and Estima, Jacinto and Martins, Bruno},
+ journal={Multimedia Tools and Applications},
+ volume={79},
+ number={35},
+ pages={26197--26223},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00044,
- author = {AL Ginsca and M Lupu and A Popescu},
- title = {User Context Mining Module},
- journal = {MUCKE Project Deliverable},
- year = {2014}
+
+@phdthesis{osailan2020topic,
+ title={Topic Modeling Location-Based Social Media Applications},
+ author={Osailan, Sarah},
+ year={2020},
+ school={The Claremont Graduate University}
}
-@article{mediaeval00050,
- author = {A Pournaras and V Mezaris and D Stein and S Eickeler and ...},
- title = {LinkedTV at MediaEval 2014 Search and Hyperlinking Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014}
+
+@article{feng2020flood,
+ title={Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey},
+ author={Feng, Yu and Brenner, Claus and Sester, Monika},
+ journal={ISPRS Journal of Photogrammetry and Remote Sensing},
+ volume={169},
+ pages={301--319},
+ year={2020},
+ publisher={Elsevier}
}
-@article{mediaeval00053,
- author = {P Galuščáková and P Pecina},
- title = {CUNI at MediaEval 2014 search and hyperlinking task: Search task experiments},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{zhu2020affective,
+ title={Affective Video Content Analysis via Multimodal Deep Quality Embedding Network},
+ author={Zhu, Yaochen and Chen, Zhenzhong and Wu, Feng},
+ journal={IEEE Transactions on Affective Computing},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00281,
- author = {E Acar and S Albayrak},
- title = {TUB-IRML at MediaEval 2014 Violent Scenes Detection Task: Violence Modeling through Feature Space Partitioning.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{gu2020violent,
+ title={Violent Video Detection Based on Semantic Correspondence},
+ author={Gu, Chaonan and Wu, Xiaoyu and Wang, Shengjin},
+ journal={IEEE Access},
+ volume={8},
+ pages={85958--85967},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00282,
- author = {AL Gînsca and A Popescu and N Rekabsaz},
- title = {CEA LIST's Participation at the MediaEval 2014 Retrieving Diverse Social Images Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{maseri2020socio,
+ title={Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping},
+ author={Maseri, Aimi Nadrah and Norman, Azah Anir and Eke, Christopher Ifeanyi and Ahmad, Atif and Molok, Nurul Nuha Abdul},
+ journal={IEEE Access},
+ volume={8},
+ pages={92929--92944},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00283,
- author = {A Buzo and H Cucu and C Burileanu},
- title = {SpeeD@ MediaEval 2014: Spoken Term Detection with Robust Multilingual Phone Recognition.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{quan2020flood,
+ title={Flood level prediction via human pose estimation from social media images},
+ author={Quan, Khanh-An C and Nguyen, Vinh-Tiep and Nguyen, Tan-Cong and Nguyen, Tam V and Tran, Minh-Triet},
+ booktitle={Proceedings of the 2020 International Conference on Multimedia Retrieval},
+ pages={479--485},
+ year={2020}
}
-@article{mediaeval00284,
- author = {D Racca and GJF Jones},
- title = {Dcu search runs at MediaEval 2014 search and hyperlinking},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
-}
-@article{mediaeval00285,
- author = {C Gracia and X Anguera and X Binefa},
- title = {Combining temporal and spectral information for Query-by-Example Spoken Term Detection},
- journal = {IEEE European Signal Processing Conference (EUSIPCO)},
- year = {2014},
+
+@inproceedings{khan2020interactive,
+ title={An Interactive Learning System for Large-Scale Multimedia Analytics},
+ author={Khan, Omar Shahbaz},
+ booktitle={Proceedings of the 2020 International Conference on Multimedia Retrieval},
+ pages={368--372},
+ year={2020}
}
-@article{mediaeval00286,
- author = {X Anguera and LJ Rodriguez-Fuentes and I Szőke and A Buzo and F Metze and M Penagarikano},
- title = {Query-by-example spoken term detection on multilingual unconstrained speech.},
- journal = {Annual Conference of the International Speech Communication Association},
- year = {2014},
+
+@article{tahmasebzadeh2020feature,
+ title={A Feature Analysis for Multimodal News Retrieval},
+ author={Tahmasebzadeh, Golsa and Hakimov, Sherzod and M{\"u}ller-Budack, Eric and Ewerth, Ralph},
+ journal={arXiv preprint arXiv:2007.06390},
+ year={2020}
}
-@article{mediaeval00287,
- author = {H Fradi and Y Yan and JL Dugelay},
- title = {Privacy Protection Filter Using Shape and Color Cues.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{ali2019classification,
+title = {Classification Performance of Violence Content by Deep Neural Network with Monarch Butterfly Optimization},
+journal = {International Journal of Advanced Computer Science and Applications},
+year = {2019},
+publisher = {The Science and Information Organization},
+volume = {10},
+number = {12},
+author = {Ashikin Ali and Norhalina Senan and Iwan Tri Riyadi Yanto and Saima Anwar Lashari}
}
-@article{mediaeval00288,
- author = {M Riga and G Petkos and S Papadopoulos and M Schinas and ...},
- title = {CERTH@ MediaEval 2014 Social Event Detection Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{sanchez2020exploiting,
+ title={Exploiting vulnerabilities of deep neural networks for privacy protection},
+ author={Sanchez-Matilla, Ricardo and Li, Chau Yi and Shamsabadi, Ali Shahin and Mazzon, Riccardo and Cavallaro, Andrea},
+ journal={IEEE Transactions on Multimedia},
+ volume={22},
+ number={7},
+ pages={1862--1873},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00289,
- author = {RT Calumby and VP Santana and FS Cordeiro and OAB Penatti and LT Li and G Chiachia and RdaS Torres and others},
- title = {Recod@ MediaEval 2014: diverse social images retrieval.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{schwarz2020emet,
+ title={EMET: Embeddings from Multilingual-Encoder Transformer for Fake News Detection},
+ author={Schwarz, Stephane and The{\'o}philo, Ant{\^o}nio and Rocha, Anderson},
+ booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={2777--2781},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00290,
- author = {W Bailer and W Weiss and C Schober and G Thallinger},
- title = {Browsing linked video collections for media production},
- journal = {International Conference on Multimedia Modeling},
- year = {2014},
+
+@inproceedings{peixoto2020multimodal,
+ title={Multimodal Violence Detection in Videos},
+ author={Peixoto, Bruno and Lavi, Bahram and Bestagini, Paolo and Dias, Zanoni and Rocha, Anderson},
+ booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={2957--2961},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00291,
- author = {P Galuščáková and M Kruliš and J Lokoč and P Pecina},
- title = {Cuni at mediaeval 2014 search and hyperlinking task: visual and prosodic features in hyperlinking.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{cstefan2020system,
+ title={System Fusion with Deep Ensembles},
+ author={{\c{S}}tefan, Liviu-Daniel and Constantin, Mihai Gabriel and Ionescu, Bogdan},
+ booktitle={Proceedings of the 2020 International Conference on Multimedia Retrieval},
+ pages={256--260},
+ year={2020}
}
-@article{mediaeval00292,
- author = {T Collins},
- title = {Stravinsqi/De Montfort University at the MediaEval 2014 C@merata Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{chandra2020preliminary,
+ title={Preliminary Investigation of Across-Body Vibrotactile Pattern for the Design of Affective Furniture},
+ author={Chandra, Yurike and Tag, Benjamin and Peiris, Roshan Lalintha and Minamizawa, Kouta},
+ booktitle={2020 IEEE Haptics Symposium (HAPTICS)},
+ pages={671--676},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00293,
- author = {Á Erdélyi and T Winkler and B Rinner},
- title = {Multi-Level Cartooning for Context-Aware Privacy Protection in Visual Sensor Networks.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{shamsabadi2020edgefool,
+ title={EdgeFool: An Adversarial Image Enhancement Filter},
+ author={Shamsabadi, Ali Shahin and Oh, Changjae and Cavallaro, Andrea},
+ booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={1898--1902},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00294,
- author = {E Sansone and G Boato and MS Dao},
- title = {Synchronizing Multi-User Photo Galleries with MRF.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{martin2020fine,
+ title={Fine grained sport action recognition with Twin spatio-temporal convolutional neural networks},
+ author={Martin, Pierre-Etienne and Benois-Pineau, Jenny and P{\'e}teri, Renaud and Morlier, Julien},
+ journal={Multimedia Tools and Applications},
+ volume={79},
+ number={27},
+ pages={20429--20447},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00295,
- author = {S Kesiraju and GV Mantena and K Prahallad},
- title = {IIIT-H System for MediaEval 2014 QUESST.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{russo2020cochleogram,
+ title={Cochleogram-based approach for detecting perceived emotions in music},
+ author={Russo, Mladen and Kraljevi{\'c}, Luka and Stella, Maja and Sikora, Marjan},
+ journal={Information Processing \& Management},
+ volume={57},
+ number={5},
+ pages={102270},
+ year={2020},
+ publisher={Elsevier}
}
-@article{mediaeval00296,
- author = {M Calvo and M Gim{\'e}nez and LF Hurtado and ES Arnal and JA G{\'o}mez},
- title = {ELiRF at MediaEval 2014: Query by Example Search on Speech Task (QUESST).},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{ionescu2020benchmarking,
+ title={Benchmarking Image Retrieval Diversification Techniques for Social Media},
+ author={Ionescu, Bogdan and Rohm, Maia and Boteanu, Bogdan and G{\^\i}nsc{\k{a}}, Alexandru Lucian and Lupu, Mihai and Mueller, Henning},
+ journal={IEEE Transactions on Multimedia},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00297,
- author = {W Bailer and M Lokaj and H Stiegler},
- title = {Context in Video Search: Is Close-by Good Enough When Using Linking?},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2014},
+@article{constantin2020affect,
+ title={Affect in multimedia: Benchmarking violent scenes detection},
+ author={Constantin, Mihai Gabriel and Stefan, Liviu Daniel and Ionescu, Bogdan and Demarty, Claire-H{\'e}lene and Sjoberg, Mats and Schedl, Markus and Gravier, Guillaume},
+ journal={IEEE Transactions on Affective Computing},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00298,
- author = {AR Simon and G Gravier and P S{\'e}billot and MF Moens},
- title = {IRISA and KUL at MediaEval 2014: Search and hyperlinking task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{du2020dynamic,
+ title={Dynamic Music emotion recognition based on CNN-BiLSTM},
+ author={Du, Pengfei and Li, Xiaoyong and Gao, Yali},
+ booktitle={2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)},
+ pages={1372--1376},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00299,
- author = {M Zaharieva and M Riegler and M Del Fabro},
- title = {Multimodal Synchronization of Image Galleries.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{bouhlel2020visual,
+ title={Visual Re-Ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval},
+ author={Bouhlel, Noura and Feki, Ghada and Amar, Chokri Ben},
+ booktitle={European Conference on Information Retrieval},
+ pages={511--526},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00300,
- author = {S Denman and D Dean and C Fookes and S Sridharan},
- title = {SAIVT-ADMRG@ mediaeval 2014 social event detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{goynuk2020supervised,
+ title={Supervised learning methods for diversification of image search results},
+ author={Goynuk, Burak and Altingovde, Ismail Sengor},
+ booktitle={European Conference on Information Retrieval},
+ pages={158--165},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00301,
- author = {C Spampinato and S Palazzo},
- title = {PeRCeiVe Lab@ UNICT at MediaEval 2014 Diverse Images: Random Forests for Diversity-based Clustering.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{formal2020learning,
+ title={Learning to Rank Images with Cross-Modal Graph Convolutions},
+ author={Formal, Thibault and Clinchant, St{\'e}phane and Renders, Jean-Michel and Lee, Sooyeol and Cho, Geun Hee},
+ booktitle={European Conference on Information Retrieval},
+ pages={589--604},
+ year={2020},
+ organization={Springer}
}
-@article{mediaeval00302,
- author = {K Markov and T Matsui},
- title = {Dynamic Music Emotion Recognition Using State-Space Models.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{medina2020emotional,
+ title={Emotional classification of music using neural networks with the MediaEval dataset},
+ author={Medina, Yesid Ospitia and Beltr{\'a}n, Jos{\'e} Ram{\'o}n and Baldassarri, Sandra},
+ journal={Personal and Ubiquitous Computing},
+ pages={1--13},
+ year={2020},
+ publisher={Springer}
+}
+@article{qian2020last,
+ title={Last: Location-appearance-semantic-temporal clustering based POI summarization},
+ author={Qian, Xueming and Wu, Yuxia and Li, Mingdi and Ren, Yayun and Jiang, Shuhui and Li, Zhetao},
+ journal={IEEE Transactions on Multimedia},
+ year={2020},
+ publisher={IEEE}
}
-@article{mediaeval00303,
- author = {JRM Palotti and N Rekabsaz and M Lupu and A Hanbury},
- title = {TUW@ Retrieving Diverse Social Images Task 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{atto2020timed,
+ title={Timed-image based deep learning for action recognition in video sequences},
+ author={Atto, Abdourrahmane Mahamane and Benoit, Alexandre and Lambert, Patrick},
+ journal={Pattern Recognition},
+ volume={104},
+ pages={107353},
+ year={2020},
+ publisher={Elsevier}
}
-@article{mediaeval00304,
- author = {D Manchon-Vizuete and I Gris-Sarabia and XG Nieto},
- title = {Photo clustering of social events by extending PhotoTOC to a rich context.},
- journal = {ICMR Workshop on Social Events in Web Multimedia},
- year = {2014},
+
+@inproceedings{may2019familiar,
+ title={Familiar Feelings: Listener-Rated Familiarity in Music Emotion Recognition},
+ author={May, Lloyd and Casey, Michael},
+ booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
+ pages={446--453},
+ year={2019},
+ organization={Springer}
}
-@article{mediaeval00305,
- author = {MS Dao and AD Duong and FG De Natale},
- title = {Unsupervised social media events clustering using user-centric parallel split-n-merge algorithms.},
- journal = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2014},
+
+@article{sorussa2020emotion,
+ title={Emotion Classification System for Digital Music with a Cascaded Technique},
+ author={Sorussa, Kanawat and Choksuriwong, Anant and Karnjanadecha, Montri},
+ journal={ECTI Transactions on Computer and Information Technology (ECTI-CIT)},
+ volume={14},
+ number={1},
+ pages={53--66},
+ year={2020}
}
-@article{mediaeval00306,
- author = {S Wan},
- title = {The CLAS System at the MediaEval 2014 C@merata Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{zhou2020survey,
+ title={A survey on multi-modal social event detection},
+ author={Zhou, Han and Yin, Hongpeng and Zheng, Hengyi and Li, Yanxia},
+ journal={Knowledge-Based Systems},
+ volume={195},
+ pages={105695},
+ year={2020},
+ publisher={Elsevier}
}
-@article{mediaeval00307,
- author = {A Aljanaki and M Soleymani and F Wiering and R Veltkamp},
- title = {Mediaeval 2014: A multimodal approach to drop detection in electronic dance music.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{cao2020exploring,
+ title={Exploring the role of visual content in fake news detection},
+ author={Cao, Juan and Qi, Peng and Sheng, Qiang and Yang, Tianyun and Guo, Junbo and Li, Jintao},
+ journal={Disinformation, Misinformation, and Fake News in Social Media},
+ pages={141--161},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00308,
- author = {P Nowak and M Thaler and H Stiegler and W Bailer},
- title = {JRS at Event Synchronization Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{jain2020automatic,
+ title={Automatic flood detection in SentineI-2 images using deep convolutional neural networks},
+ author={Jain, Pallavi and Schoen-Phelan, Bianca and Ross, Robert},
+ booktitle={Proceedings of the 35th Annual ACM Symposium on Applied Computing},
+ pages={617--623},
+ year={2020}
+}
+@article{schaible2020evaluation,
+ title={Evaluation Infrastructures for Academic Shared Tasks},
+ author={Schaible, Johann and Breuer, Timo and Tavakolpoursaleh, Narges and M{\"u}ller, Bernd and Wolff, Benjamin and Schaer, Philipp},
+ journal={Datenbank-Spektrum},
+ pages={1--8},
+ year={2020},
+ publisher={Springer}
}
-@article{mediaeval00309,
- author = {H Wang and T Lee},
- title = {CUHK System for QUESST Task of MediaEval 2014.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{kang2020multi,
+ title={Multi-modal component embedding for fake news detection},
+ author={Kang, SeongKu and Hwang, Junyoung and Yu, Hwanjo},
+ booktitle={2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM)},
+ pages={1--6},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00310,
- author = {W Yang and K Cai and B Wu and Y Wang and X Chen and D Yang and A Horner},
- title = {Beatsens' Solution for MediaEval 2014 Emotion in Music Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{xie2020musical,
+ title={Musical emotion recognition with spectral feature extraction based on a sinusoidal model with model-based and deep-learning approaches},
+ author={Xie, Baijun and Kim, Jonathan C and Park, Chung Hyuk},
+ journal={Applied Sciences},
+ volume={10},
+ number={3},
+ pages={902},
+ year={2020},
+ publisher={Multidisciplinary Digital Publishing Institute}
}
-@article{mediaeval00311,
- author = {Z Paróczi and B Fodor and G Szücs},
- title = {DCLab at MediaEval2014 Search and Hyperlinking Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@inproceedings{cheuk2020regression,
+ title={Regression-based music emotion prediction using triplet neural networks},
+ author={Cheuk, Kin Wai and Luo, Yin-Jyun and Balamurali, BT and Roig, Gemma and Herremans, Dorien},
+ booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
+ pages={1--7},
+ year={2020},
+ organization={IEEE}
}
-@article{mediaeval00312,
- author = {AR Simon and G Gravier and P Sébillot},
- title = {IRISA at MediaEval 2015: Search and Anchoring in Video Archives Task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{bouhlel2020hypergraph,
+ title={Hypergraph learning with collaborative representation for image search reranking},
+ author={Bouhlel, Noura and Feki, Ghada and Ammar, Anis Ben and Amar, Chokri Ben},
+ journal={International Journal of Multimedia Information Retrieval},
+ pages={1--10},
+ year={2020},
+ publisher={Springer}
+}
+@article{lopez2020statistical,
+ title={Statistical language models for query-by-example spoken document retrieval},
+ author={Lopez-Otero, Paula and Parapar, Javier and Barreiro, Alvaro},
+ journal={Multimedia Tools and Applications},
+ volume={79},
+ number={11},
+ pages={7927--7949},
+ year={2020},
+ publisher={Springer}
+}
+@inproceedings{guo2020global,
+ title={Global Affective Video Content Regression Based on Complementary Audio-Visual Features},
+ author={Guo, Xiaona and Zhong, Wei and Ye, Long and Fang, Li and Heng, Yan and Zhang, Qin},
+ booktitle={International Conference on Multimedia Modeling},
+ pages={540--550},
+ year={2020},
+ organization={Springer}
}
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- author = {N Kini},
- title = {TCSL at the MediaEval 2014 C@merata Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{borgli2020hyperkvasir,
+ title={HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
+ author={Borgli, Hanna and Thambawita, Vajira and Smedsrud, Pia H and Hicks, Steven and Jha, Debesh and Eskeland, Sigrun L and Randel, Kristin Ranheim and Pogorelov, Konstantin and Lux, Mathias and Nguyen, Duc Tien Dang and others},
+ journal={Scientific Data},
+ volume={7},
+ number={1},
+ pages={1--14},
+ year={2020},
+ publisher={Nature Publishing Group}
}
-@article{mediaeval00314,
- author = {P Korshunov and T Ebrahimi},
- title = {Mediaeval 2014 visual privacy task: geometrical privacy protection tool},
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- year = {2014},
+@article{yang2019shared,
+ title={Shared Multi-view Data Representation for Multi-domain Event Detection},
+ author={Z Yang and Q Li and L Wenyin and J Lv},
+ journal={IEEE transactions on pattern analysis and machine intelligence},
+ year={2019},
+ publisher={IEEE}
}
-@article{mediaeval00025,
- author = {H Mousselly-Sergieh and D Watzinger and B Huber and M Döller and E Egyed-Zsigmond and H Kosch},
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- journal = {ACM multimedia systems conference},
- year = {2014}
+@inproceedings{pereira2019assessing,
+ title={Assessing flood severity from georeferenced photos},
+ author={Pereira, Jorge and Monteiro, Jo{\~a}o and Estima, Jacinto and Martins, Bruno},
+ booktitle={Proceedings of the 13th Workshop on Geographic Information Retrieval},
+ pages={1--10},
+ year={2019}
}
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- journal = {MUCKE Project Deliverable},
- year = {2014}
+@phdthesis{goynuk2019supervised,
+ title={Supervised Learning for Image Search Result Diversification},
+ author={G{\"O}YN{\"U}K, Burak},
+ year={2019},
+ school={Middle East Technical University}
}
-@article{mediaeval00042,
- author = {C Gracia and X Anguera and X Binefa},
- title = {Combining temporal and spectral information for Query-by-Example Spoken Term Detection},
- journal = {European Signal Processing Conference (EUSIPCO)},
- year = {2014}
+@mastersthesis{sandberg2019music,
+ title={Music and Sport: An Explorative Study using Unsupervised Machine Learning},
+ author={Sandberg, Marius Alexander},
+ year={2019}
}
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- title = {UNIZA@ Mediaeval 2014 Visual Privacy Task: Object Transparency Approach.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@inproceedings{jony2018ensemble,
+ title={Ensemble Classification Technique for Water Detection in Satellite Images},
+ author={RI Jony and A Woodley and A Raj and D Perrin},
+ booktitle={2018 Digital Image Computing: Techniques and Applications (DICTA)},
+ pages={1--8},
+ year={2018},
+ organization={IEEE}
}
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- author = {S Schmiedeke and P Kelm and L Goldmann and T Sikora},
- title = {TUB@ MediaEval 2014 Visual Privacy Task: Reversible Scrambling on Foreground Masks.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+
+@article{mo2019short,
+ title={Short-time Emotion Tracker Using a Convolutional Autoencoder},
+ author={Y mo Yang},
+ journal={ECE Department at University of Rochester},
+ year={2019}
}
-@article{mediaeval00317,
- author = {J Hare and D Dupplaw and P Lewis and W Hall and K Martinez},
- title = {Exploiting multimedia in creating and analysing multimedia Web archives},
- journal = {Future Internet},
- year = {2014},
+@inproceedings{jony2019flood,
+ title={Flood Detection in Social Media Images using Visual Features and Metadata},
+ author={Jony, Rabiul Islam and Woodley, Alan and Perrin, Dimitri},
+ booktitle={2019 Digital Image Computing: Techniques and Applications (DICTA)},
+ pages={1--8},
+ year={2019},
+ organization={IEEE}
+}
+
+@inproceedings{liu2019music,
+ title={Music Emotion Recognition Using a Variant of Recurrent Neural Network},
+ author={H Liu and Y Fang and Q Huang},
+ booktitle={2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application (MMSSA 2018)},
+ year={2019},
+ organization={Atlantis Press}
+}
+
+@misc{fukayama2019system,
+ title={System, method, and computer program for estimation of target value},
+ author={S Fukayama and M Goto},
+ year={2019},
+ booktitle={US Patent App. 16/070,144},
+}
+
+@article{guo2019affective,
+ title={Affective video content analysis based on multimodal data fusion in heterogeneous networks},
+ author={J Guo and B Song and P Zhang and M Ma and W Luo},
+ journal={Information Fusion},
+ volume={51},
+ pages={224--232},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{markov2014music,
+ title={Music genre and emotion recognition using Gaussian processes},
+ author={K Markov and T Matsui},
+ journal={IEEE access},
+ volume={2},
+ pages={688--697},
+ year={2014},
+ publisher={IEEE}
+}
+
+@article{muszynski2019recognizing,
+ title={Recognizing Induced Emotions of Movie Audiences From Multimodal Information},
+ author={M Muszynski and L Tian and C Lai and J Moore and T Kostoulas and P Lombardo and T Pun and G Chanel},
+ journal={IEEE Transactions on Affective Computing},
+ year={2019},
+ publisher={IEEE}
+}
+
+@phdthesis{muszynski2018recognizing,
+ title={Recognizing film aesthetics, spectators' affect and aesthetic emotions from multimodal signals},
+ author={M Muszynski},
+ year={2018},
+ school={University of Geneva},
+ journal={PhD Thesis}
+}
+
+@article{gialampoukidis2019multimodal,
+ title={Multimodal Fusion of Big Multimedia Data},
+ author={I Gialampoukidis and E Chatzilari and S Nikolopoulos and S Vrochidis and I Kompatsiaris},
+ journal={Big Data Analytics for Large-Scale Multimedia Search},
+ pages={121},
+ year={2019},
+ publisher={John Wiley \& Sons}
+}
+
+@article{larson2019privacy,
+ title={Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger},
+ author={M Larson and J Choi and M Slokom and Z Erkin and G Friedland and AP de Vries},
+ journal={Big Data Analytics for Large-Scale Multimedia Search},
+ pages={183},
+ year={2019},
+ publisher={John Wiley \& Sons}
+}
+
+@article{bischke2019large,
+ title={Large-Scale Social Multimedia Analysis},
+ author={B Bischke and D Borth and A Dengel},
+ journal={Big Data Analytics for Large-Scale Multimedia Search},
+ pages={157},
+ year={2019},
+ publisher={John Wiley \& Sons}
+}
+
+@phdthesis{khattar2019neural,
+ title={Neural Approaches Towards Computational Journalism},
+ author={D Khattar},
+ year={2019},
+ school={International Institute of Information Technology Hyderabad},
+ journal={PhD Thesis}
+}
+
+@article{madhavi2019vocal,
+ title={Vocal Tract Length Normalization using a Gaussian mixture model framework for query-by-example spoken term detection},
+ author={MC Madhavi and HA Patil},
+ journal={Computer Speech \& Language},
+ volume={58},
+ pages={175--202},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{moumtzidouroad,
+ title={Road passability estimation using deep neural networks and sattelite image patches},
+ author={A Moumtzidou and M Bakratsas and S Andreadis and I Gialampoukidis and S Vrochidis and I Kompatsiaris},
+ year={2019}
+}
+
+@inproceedings{li2019scene,
+ title={Scene privacy protection},
+ author={CY Li and AS Shamsabadi and R Sanchez-Matilla and R Mazzon and A Cavallaro},
+ booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={2502--2506},
+ year={2019},
+ organization={IEEE}
+}
+
+@inproceedings{engilberge2019sodeep,
+ title={SoDeep: a Sorting Deep net to learn ranking loss surrogates},
+ author={M Engilberge and L Chevallier and P P{\'e}rez and M Cord},
+ booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={10792--10801},
+ year={2019}
+}
+
+@article{ibrahim2019large,
+ title={Large-scale Text-based Video Classification using Contextual Features},
+ author={ZAA Ibrahim and S Haidar and I Sbeity},
+ journal={European Journal of Electrical Engineering and Computer Science},
+ volume={3},
+ number={2},
+ year={2019}
+}
+
+@article{lago2019visual,
+ title={Visual and Textual Analysis for Image Trustworthiness Assessment within Online News},
+ author={F Lago and QT Phan and G Boato},
+ journal={Security and Communication Networks},
+ volume={2019},
+ year={2019},
+ publisher={Hindawi}
+}
+
+@inproceedings{peixoto2019toward,
+ title={Toward Subjective Violence Detection in Videos},
+ author={B Peixoto and B Lavi and JPP Martin and S Avila and Z Dias and A Rocha},
+ booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={8276--8280},
+ year={2019},
+ organization={IEEE}
+}
+
+@article{wang2019video,
+ title={Video Affective Content Analysis by Exploring Domain Knowledge},
+ author={S Wang and C Wang and T Chen and Y Wang and Y Shu and Q Ji},
+ journal={IEEE Transactions on Affective Computing},
+ year={2019},
+ publisher={IEEE}
+}
+
+@inproceedings{khattar2019mvae,
+ title={MVAE: Multimodal Variational Autoencoder for Fake News Detection},
+ author={Khattar, Dhruv and Goud, Jaipal Singh and Gupta, Manish and Varma, Vasudeva},
+ booktitle={The World Wide Web Conference},
+ pages={2915--2921},
+ year={2019},
+ organization={ACM}
+}
+
+@article{yuan2019diversified,
+ title={Diversified textual features based image retrieval},
+ author={Yuan, Bo and Gao, Xinbo},
+ journal={Neurocomputing},
+ volume={357},
+ pages={116--124},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{benavent2019fca,
+ title={FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images},
+ author={Benavent, Xaro and Castellanos, Angel and de Ves, Esther and Garc{\'\i}a-Serrano, Ana and Cigarr{\'a}n, Juan},
+ journal={Future Generation Computer Systems},
+ volume={100},
+ pages={250--265},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@inproceedings{lux2019summarizing,
+ title={Summarizing E-Sports Matches and Tournaments},
+ author={Lux, Mathias and Halvorsen, P{\aa}l and Dang-Nguyen, Duc-Tien and Stensland, H{\aa}kon and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael},
+ booktitle={Workshop on Immersive Mixed and Virtual Environment Systems, Amherst, MA, USA},
+ year={2019}
+}
+
+@article{dong2019bidirectional,
+ title={Bidirectional Convolutional Recurrent Sparse Network (BCRSN): An Efficient Model for Music Emotion Recognition},
+ author={Dong, Yizhuo and Yang, Xinyu and Zhao, Xi and Li, Juan},
+ journal={IEEE Transactions on Multimedia},
+ year={2019},
+ publisher={IEEE}
+}
+
+@article{potthast2019tira,
+ title={TIRA Integrated Research Architecture},
+ author={Potthast, Martin and Gollub, Tim and Wiegmann, Matti and Stein, Benno},
+ journal={Information Retrieval Evaluation in a Changing World-Lessons Learned from},
+ volume={20},
+ year={2019}
+}
+
+@inproceedings{dourado2019event,
+ title={Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models},
+ author={Dourado, Icaro Cavalcante and Tabbone, Salvatore and da Silva Torres, Ricardo},
+ booktitle={International Workshop on Graph-Based Representations in Pattern Recognition},
+ pages={88--98},
+ year={2019},
+ organization={Springer}
+}
+
+@inproceedings{orjesek2019dnn,
+ title={DNN Based Music Emotion Recognition from Raw Audio Signal},
+ author={Orjesek, Richard and Jarina, Roman and Chmulik, Michal and Kuba, Michal},
+ booktitle={2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA)},
+ pages={1--4},
+ year={2019},
+ organization={IEEE}
+}
+
+@article{wu2019does,
+ title={Does Diversity Affect User Satisfaction in Image Search},
+ author={Wu, Zhijing and Zhou, Ke and Liu, Yiqun and Zhang, Min and Ma, Shaoping},
+ journal={ACM Transactions on Information Systems (TOIS)},
+ volume={37},
+ number={3},
+ pages={35},
+ year={2019},
+ publisher={ACM}
+}
+
+@article{saravi2019use,
+ title={Use of artificial intelligence to improve resilience and preparedness against adverse flood events},
+ author={Saravi, Sara and Kalawsky, Roy and Joannou, Demetrios and Rivas-Casado, Monica and Fu, Guangtao and Meng, Fanlin},
+ journal={Water},
+ volume={11},
+ number={5},
+ pages={973},
+ year={2019},
+ publisher={Multidisciplinary Digital Publishing Institute}
+}
+
+@inproceedings{dourado2019event,
+ title={Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models},
+ author={Dourado, Icaro Cavalcante and Tabbone, Salvatore and da Silva Torres, Ricardo},
+ booktitle={International Workshop on Graph-Based Representations in Pattern Recognition},
+ pages={88--98},
+ year={2019},
+ organization={Springer}
+}
+
+@inproceedings{lux2019summarizing,
+ title={Summarizing E-sports matches and tournaments: the example of counter-strike: global offensive},
+ author={Lux, Mathias and Halvorsen, P{\aa}l and Dang-Nguyen, Duc-Tien and Stensland, H{\aa}kon and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael},
+ booktitle={Proceedings of the 11th ACM Workshop on Immersive Mixed and Virtual Environment Systems},
+ pages={13--18},
+ year={2019},
+ organization={ACM}
+}
+
+@phdthesis{ammar2019using,
+ title={Using deep learning algorithms to detect violent activities},
+ author={Ammar, SM and Anjum, Md and Rounak, Tanvir and Islam, Md and Islam, Touhidul and others},
+ year={2019},
+ school={BRAC University},
+ journal={PhD Thesis}
+}
+
+@article{ram2019multilingual,
+ title={Multilingual Bottleneck Features for Query by Example Spoken Term Detection},
+ author={Ram, Dhananjay and Miculicich, Lesly and Bourlard, Herv{\'e}},
+ journal={arXiv preprint arXiv:1907.00443},
+ year={2019}
+}
+
+@article{abebe2019generic,
+ title={Generic metadata representation framework for social-based event detection, description, and linkage},
+ author={Abebe, Minale A and Tekli, Joe and Getahun, Fekade and Chbeir, Richard and Tekli, Gilbert},
+ journal={Knowledge-Based Systems},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{cogan2019mapgi,
+ title={MAPGI: Accurate identification of anatomical landmarks and diseased tissue in gastrointestinal tract using deep learning},
+ author={Cogan, Timothy and Cogan, Maribeth and Tamil, Lakshman},
+ journal={Computers in biology and medicine},
+ volume={111},
+ pages={103351},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@article{tejedor2019search,
+ title={Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation},
+ author={Tejedor, Javier and Toledano, Doroteo T and Lopez-Otero, Paula and Docio-Fernandez, Laura and Pe{\~n}agarikano, Mikel and Rodriguez-Fuentes, Luis Javier and Moreno-Sandoval, Antonio},
+ journal={EURASIP Journal on Audio, Speech, and Music Processing},
+ volume={2019},
+ number={1},
+ pages={13},
+ year={2019},
+ publisher={SpringerOpen}
+}
+
+@inproceedings{li2019affective,
+ title={Affective Video Content Analyses by Using Cross-Modal Embedding Learning Features},
+ author={Li, Benchao and Chen, Zhenzhong and Li, Shan and Zheng, Wei-Shi},
+ booktitle={2019 IEEE International Conference on Multimedia and Expo (ICME)},
+ pages={844--849},
+ year={2019},
+ organization={IEEE}
+}
+
+@phdthesis{kirkerod2019unsupervised,
+ title={Unsupervised preprocessing of medical imaging data with generative adversarial networks},
+ author={Kirker{\o}d, Mathias},
+ year={2019},
+ journal={Master Thesis}
+}
+
+@inproceedings{lommatzsch2019framework,
+ title={A Framework for Analyzing News Images and Building Multimedia-Based Recommender},
+ author={Lommatzsch, Andreas and Kille, Benjamin and Styp-Rekowski, Kevin and Karl, Max and Pommering, Jan},
+ booktitle={International Conference on Innovations for Community Services},
+ pages={184--201},
+ year={2019},
+ organization={Springer}
+}
+
+@article{epure2019leveraging,
+ title={Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation},
+ author={Epure, Elena V and Khlif, Anis and Hennequin, Romain},
+ journal={arXiv preprint arXiv:1907.08698},
+ year={2019}
+}
+
+@article{vishwakarma2019detection,
+ title={Detection and veracity analysis of fake news via scrapping and authenticating the web search},
+ author={Vishwakarma, Dinesh Kumar and Varshney, Deepika and Yadav, Ashima},
+ journal={Cognitive Systems Research},
+ volume={58},
+ pages={217--229},
+ year={2019},
+ publisher={Elsevier}
+}
+
+@inproceedings{chmulik2019continuous,
+ title={Continuous Music Emotion Recognition Using Selected Audio Features},
+ author={Chmulik, Michal and Jarina, Roman and Kuba, Michal and Lieskovska, Eva},
+ booktitle={2019 42nd International Conference on Telecommunications and Signal Processing (TSP)},
+ pages={589--592},
+ year={2019},
+ organization={IEEE}
+}
+
+@inproceedings{gamage2019gi,
+ title={GI-Net: anomalies classification in gastrointestinal tract through endoscopic imagery with deep learning},
+ author={Gamage, Chathurika and Wijesinghe, Isuru and Chitraranjan, Charith and Perera, Indika},
+ booktitle={2019 Moratuwa Engineering Research Conference (MERCon)},
+ pages={66--71},
+ year={2019},
+ organization={IEEE}
+}
+
+@article{wang2019knowledge,
+ title={Knowledge-Augmented Multimodal Deep Regression Bayesian Networks for Emotion Video Tagging},
+ author={Wang, Shangfei and Hao, Longfei and Ji, Qiang},
+ journal={IEEE Transactions on Multimedia},
+ volume={22},
+ number={4},
+ pages={1084--1097},
+ year={2019},
+ publisher={IEEE}
+}
+
+@article{zhang2019video,
+ title={Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context},
+ author={Zhang, Jie and Zhao, Yin and Cai, Longjun and Tu, Chaoping and Wei, Wu},
+ journal={arXiv preprint arXiv:1909.01763},
+ year={2019}
+}
+
+@techreport{le2019multimodal,
+ title={Multimodal person recognition in audio-visual streams},
+ author={Le, Do Hoang Nam},
+ year={2019},
+ institution={EPFL}
+}
+
+@inproceedings{singhal2019spotfake,
+ title={SpotFake: A Multi-modal Framework for Fake News Detection},
+ author={Singhal, Shivangi and Shah, Rajiv Ratn and Chakraborty, Tanmoy and Kumaraguru, Ponnurangam and Satoh, Shin'ichi},
+ booktitle={2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM)},
+ pages={39--47},
+ year={2019},
+ organization={IEEE}
+}
+
+@inproceedings{yusuf2019temporally,
+ title={Temporally-Aware Acoustic Unit Discovery for Zerospeech 2019 Challenge.},
+ author={Yusuf, Bolaji and G{\"o}k, Alican and G{\"u}ndogdu, Batuhan and Kose, Oyku Deniz and Saraclar, Murat},
+ booktitle={INTERSPEECH},
+ pages={1098--1102},
+ year={2019}
+}
+
+@inproceedings{sarker2019evaluation,
+ title={Evaluation of the impact of image spatial resolution in designing a context-based fully convolution neural networks for flood mapping},
+ author={Sarker, Chandrama and Mejias, Luis and Maire, Frederic and Woodley, Alan},
+ booktitle={2019 Digital Image Computing: Techniques and Applications (DICTA)},
+ pages={1--8},
+ year={2019},
+ organization={IEEE}
+}
+
+@inproceedings{de2019automatic,
+ title={An Automatic Emotion Recognition System for Annotating Spotify’s Songs},
+ author={de Quir{\'o}s, J Garc{\'\i}a and Baldassarri, Sandra and Beltr{\'a}n, Jos{\'e} Ram{\'o}n and Guiu, A and {\'A}lvarez, Pedro},
+ booktitle={OTM Confederated International Conferences" On the Move to Meaningful Internet Systems"},
+ pages={345--362},
+ year={2019},
+ organization={Springer}
+}
+
+@article{sun2019unified,
+ title={A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning},
+ author={Sun, Qiang and Wang, Liting and Li, Maohui and Zhang, Longtao and Yang, Yuxiang},
+ journal={arXiv preprint arXiv:1910.05996},
+ year={2019}
+}
+
+@inproceedings{zhu2019multimodal,
+ title={Multimodal deep denoise framework for affective video content analysis},
+ author={Zhu, Yaochen and Chen, Zhenzhong and Wu, Feng},
+ booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+ pages={130--138},
+ year={2019}
+}
+
+@inproceedings{fonseca2019multimodal,
+ title={Multimodal person discovery using label propagation over speaking faces graphs},
+ author={Fonseca, Gabriel Barbosa and Patroc{\'\i}nio Jr, Zenilton KG and Gravier, Guillaume and Guimar{\~a}es, Silvio Jamil F},
+ booktitle={Anais Estendidos da XXXII Conference on Graphics, Patterns and Images},
+ pages={126--132},
+ year={2019},
+ organization={SBC}
+}
+
+@inproceedings{shamsabadi2020edgefool,
+ title={Edgefool: an Adversarial Image Enhancement Filter},
+ author={Shamsabadi, Ali Shahin and Oh, Changjae and Cavallaro, Andrea},
+ booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ pages={1898--1902},
+ year={2020},
+ organization={IEEE}
+}
+
+@inproceedings{hoang2019enhancing,
+ title={Enhancing Endoscopic Image Classification with Symptom Localization and Data Augmentation},
+ author={Hoang, Trung-Hieu and Nguyen, Hai-Dang and Nguyen, Viet-Anh and Nguyen, Thanh-An and Nguyen, Vinh-Tiep and Tran, Minh-Triet},
+ booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+ pages={2578--2582},
+ year={2019}
+}
+
+@inproceedings{jha2020kvasir,
+ title={Kvasir-seg: A segmented polyp dataset},
+ author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{\aa}l and de Lange, Thomas and Johansen, Dag and Johansen, H{\aa}vard D},
+ booktitle={International Conference on Multimedia Modeling},
+ pages={451--462},
+ year={2020},
+ organization={Springer}
+}
+
+@inproceedings{jha2019resunet++,
+ title={Resunet++: An advanced architecture for medical image segmentation},
+ author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Johansen, Dag and De Lange, Thomas and Halvorsen, P{\aa}l and Johansen, H{\aa}vard D},
+ booktitle={2019 IEEE International Symposium on Multimedia (ISM)},
+ pages={225--2255},
+ year={2019},
+ organization={IEEE}
+}
+
+@article{ram2020neural,
+ title={Neural network based end-to-end query by example spoken term detection},
+ author={Ram, Dhananjay and Miculicich, Lesly and Bourlard, Herv{\'e}},
+ journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
+ volume={28},
+ pages={1416--1427},
+ year={2020},
+ publisher={IEEE}
+}
+
+@inproceedings{aslan2019emotion,
+ title={Emotion Prediction in Movies Using Visual Features \& Genre Information},
+ author={Aslan, Fatih and Ekenel, Haz{\i}m Kemal},
+ booktitle={2019 4th International Conference on Computer Science and Engineering (UBMK)},
+ pages={1--5},
+ year={2019},
+ organization={IEEE}
+}
+
+@incollection{lux2019challenges,
+ title={Challenges for Multimedia Research in E-Sports Using Counter-Strike},
+ author={Lux, Mathias and Riegler, Michael and Halvorsen, Pal and Dang-Nguyen, Duc-Tien and Potthast, Martin},
+ booktitle={Savegame},
+ pages={197--206},
+ year={2019},
+ publisher={Springer}
+}
+
+@inproceedings{pereira2019assessing,
+ title={Assessing flood severity from georeferenced photos},
+ author={Pereira, Jorge and Monteiro, Jo{\~a}o and Estima, Jacinto and Martins, Bruno},
+ booktitle={Proceedings of the 13th Workshop on Geographic Information Retrieval},
+ pages={1--10},
+ year={2019}
+}
+
+@inproceedings{shamsabadi2020colorfool,
+ title={Colorfool: Semantic adversarial colorization},
+ author={Shamsabadi, Ali Shahin and Sanchez-Matilla, Ricardo and Cavallaro, Andrea},
+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+ pages={1151--1160},
+ year={2020}
+}
+
+@article{yi2019affective,
+ title={Affective video content analysis with adaptive fusion recurrent network},
+ author={Yi, Yun and Wang, Hanli and Li, Qinyu},
+ journal={IEEE Transactions on Multimedia},
+ volume={22},
+ number={9},
+ pages={2454--2466},
+ year={2019},
+ publisher={IEEE}
+}
+
+@article{mediaeval2019001,
+ author = {Benjamin Bischke and Patrick Helber and Simon Brugman and Erkan Basar and Zhengyu Zhao and Martha Larson and Konstantin Pogorelov},
+ title = {The Multimedia Satellite Task at MediaEval 2019.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019002,
+ author = {Mir Murtaza and Muhammad Hanif and Muhammad Atif Tahir and Muhammad Rafi},
+ title = {Ensemble and Inference based Methods for Flood Severity Estimation Using Visual Data.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019003,
+ author = {Khanh-An Quan-Chi and Tan-Cong Nguyen and Vinh-Tiep Nguyen and Minh-Triet Tran},
+ title = {Flood Event Analysis Base on Pose Estimation and Water-Related Scene Recognition.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019004,
+ author = {Stelios Andreadis and Marios Bakratsas and Panagiotis Giannakeris and Anastasia Moumtzidou and Ilias Gialampoukidis and Stefanos Vrochidis and Ioannis Kompatsiaris},
+ title = {Multimedia Analysis Techniques for Flood Detection Using Images, Articles and Satellite Imagery.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019005,
+ author = {Mirko Zaffaroni and Laura López Fuentes and Alessandro Farasin and Paolo Garza and Harald Skinnemoen},
+ title = {AI-Based Flood Event Understanding and Quantifying Using Online Media and Satellite Data.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019006,
+ author = {Pierrick Bruneau and Thomas Tamisier},
+ title = {Transfer Learning and Mixed Input Deep Neural Networks for Estimating Flood Severity in News Content.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019007,
+ author = {Hariny Ganapathy and Geetika Bandlamudi and Yamini L and Bhuvana J and T.T. Mirnalinee},
+ title = {Deep Learning Models for Estimation of Flood Severity Using Multimodal and Satellite Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019008,
+ author = {Kashif Ahmad and Konstantin Pogorelov and Mohib Ullah and Michael Riegler and Nicola Conci and Johannes Langguth and Ala Al-Fuqaha},
+ title = {Multi-Modal Machine Learning for Floods Detection in News, Social Media and Satellite Sequences.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019009,
+ author = {Yu Feng and Shumin Tang and Hao Cheng and Monika Sester},
+ title = {Flood Level Estimation from News Articles and Flood Detection from Satellite Image Sequences.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019010,
+ author = {Dan Bînă and George-Alexandru Vlad and Cristian Onose and Dumitru-Clementin Cercel},
+ title = {Flood Severity Estimation in News Articles Using Deep Learning Approaches.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019011,
+ author = {Julia Strebl and Djordje Slijepcevic and Armin Kirchknopf and Muntaha Sakeena and Markus Seidl and Matthias Zeppelzauer},
+ title = {Estimation of Flood Level from Social Media Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019012,
+ author = {Pallavi Jain and Bianca Schoen-Phelan and Robert Ross},
+ title = {MediaEval2019: Flood Detection in Time Sequence Satellite Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019013,
+ author = {Benjamin Bischke and Simon Brugman and Patrick Helber},
+ title = {Flood Severity Estimation from Online News Images and Multi-Temporal Satellite Images Using Deep Neural Networks.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019014,
+ author = {Mihai Gabriel Constantin and Bogdan Ionescu and Claire-Hélène Demarty and Ngoc Q. K. Duong and Xavier Alameda-Pineda and Mats Sjöberg},
+ title = {The Predicting Media Memorability Task at MediaEval 2019.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019015,
+ author = {David Azcona and Enric Moreu and Feiyan Hu and Tomás E. Ward and Alan F. Smeaton},
+ title = {Predicting Media Memorability Using Ensemble Models.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019016,
+ author = {Le-Vu Tran and Vinh-Loc Huynh and Minh-Triet Tran},
+ title = {Predicting Media Memorability Using Deep Features with Attention and Recurrent Network.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019017,
+ author = {Alison Reboud and Ismail Harrando and Jorma Laaksonen and Danny Francis and Raphaël Troncy and Héctor Laria Mantecón},
+ title = {Combining Textual and Visual Modeling for Predicting Media Memorability.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019018,
+ author = {Samuel Felipe dos Santos and Jurandy Almeida},
+ title = {GIBIS at MediaEval 2019: Predicting Media Memorability Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019019,
+ author = {Alexander Viola and Sejong Yoon},
+ title = {A Hybrid Approach for Video Memorability Prediction.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019020,
+ author = {Shuai Wang and Linli Yao and Jieting Chen and Qin Jin},
+ title = {RUC at MediaEval 2019: Video Memorability Prediction Based on Visual Textual and Concept Related Features.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019021,
+ author = {Roberto Leyva and Faiyaz Doctor and Alba G. Seco de Herrera and Sohail Sahab},
+ title = {Multimodal Deep Features Fusion for Video Memorability Prediction.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019022,
+ author = {Mihai Gabriel Constantin and Chen Kang and Gabriela Dinu and Frédéric Dufaux and Giuseppe Valenzise and Bogdan Ionescu},
+ title = {Using Aesthetics and Action Recognition-Based Networks for the Prediction of Media Memorability.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019023,
+ author = {Shengzhou Yi and Xueting Wang and Toshihiko Yamasaki},
+ title = {Emotion and Theme Recognition of Music Using Convolutional Neural Networks.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019024,
+ author = {Shahin Amiriparian and Maurice Gerczuk and Eduardo Coutinho and Alice Baird and Sandra Ottl and Manuel Milling and Björn Schuller},
+ title = {Emotion and Themes Recognition in Music Utilising Convolutional and Recurrent Neural Networks.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019025,
+ author = {Hsiao-Tzu Hung and Yu-Hua Chen and Maximilian Mayerl and Michael Vötter and Eva Zangerle and Yi-Hsuan Yang},
+ title = {MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019026,
+ author = {Dmitry Bogdanov and Alastair Porter and Philip Tovstogan and Minz Won},
+ title = {MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019027,
+ author = {Maximilian Mayerl and Michael Vötter and Hsiao-Tzu Hung and Boyu Chen and Yi-Hsuan Yang and Eva Zangerle},
+ title = {Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019028,
+ author = {Khaled Koutini and Shreyan Chowdhury and Verena Haunschmid and Hamid Eghbal-Zadeh and Gerhard Widmer},
+ title = {Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019029,
+ author = {Manoj Sukhavasi and Sainath Adapa},
+ title = {Music Theme Recognition Using CNN and Self-Attention.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019030,
+ author = {Steven Hicks and Pål Halvorsen and Trine B. Haugen and Jorunn M. Andersen and Oliwia Witczak and Konstantin Pogorelov and Hugo L. Hammer and Duc-Tien Dang-Nguyen and Mathias Lux and Michael Riegler},
+ title = {Medico Multimedia Task at MediaEval 2019.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019031,
+ author = {Steven Hicks and Pål Halvorsen and Trine B. Haugen and Jorunn M. Andersen and Oliwia Witczak and Konstantin Pogorelov and Hugo L. Hammer and Duc-Tien Dang-Nguyen and Mathias Lux and Michael Riegler},
+ title = {Predicting Sperm Motility and Morphology Using Deep Learning and Handcrafted Features.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019032,
+ author = {Steven Hicks and Trine B. Haugen and Pål Halvorsen and Michael Riegler},
+ title = {Using Deep Learning to Predict Motility and Morphology of Human Sperm.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019033,
+ author = {Vajira Thambawita and Pål Halvorsen and Hugo Hammer and Michael Riegler and Trine B. Haugen},
+ title = {Stacked Dense Optical Flows and Dropout Layers to Predict Sperm Motility and Morphology.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019034,
+ author = {Vajira Thambawita and Pål Halvorsen and Hugo Hammer and Michael Riegler and Trine B. Haugen},
+ title = {Extracting Temporal Features into a Spatial Domain Using Autoencoders for Sperm Video Analysis.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019035,
+ author = {Jon-Magnus Rosenblad and Steven Hicks and Håkon Kvale Stensland and Trine B. Haugen and Pål Halvorsen and Michael Riegler},
+ title = {Using 2D and 3D Convolutional Neural Networks to Predict Semen Quality.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019036,
+ author = {Minh-Son Dao and Peijiang Zhao and Tomohiro Sato andKoji Zettsu and Duc-Tien Dang-Nguyen and Cathal Gurrin and Ngoc-Thanh Nguyen},
+ title = {Overview of MediaEval 2019: Insights for Wellbeing TaskMultimodal Personal Health Lifelog Data Analysis.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019037,
+ author = {Tu-Khiem Le and Van-Tu Ninh and Liting Zhou and Duc-Tien Dang-Nguyen and Cathal Gurrin},
+ title = {DCU Team at the 2019 Insight for Wellbeing Task: Multimodal Personal Health Lifelog Data Analysis.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019038,
+ author = {Hoang-Anh Le and Thang-Long Nguyen-Ho and Minh-Triet Tran},
+ title = {HCMUS at Insight for Wellbeing Task 2019: Multimodal Personal Health Lifelog Data Analysis with Inference from Multiple Sources and Attributes.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019039,
+ author = {Qi Huang and Ailin Sheng and Lei Pang and Xiaoyong Wei and Ramesh Jain},
+ title = {Healthism@MediaEval 2019 - Insights for Wellbeing Task: Factors Related to Subjective and Objective Health.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019040,
+ author = {Loc Tai Nguyen Tan and Minh-Tam Nguyen and Dang-Hieu Nguyen},
+ title = {Predicting Missing Data by Using Multimodal Data Analytics.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019041,
+ author = {Dang-Hieu Nguyen and Minh-Tam Nguyen and Loc Tai Nguyen Tan},
+ title = {Leveraging Egocentric and Surrounding Environment Data to Adaptively Measure a Personal Air Qality Index.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019042,
+ author = {Zhuoran Liu and Zhengyu Zhao and Martha Larson},
+ title = {Pixel Privacy 2019: Protecting Sensitive Scene Information in Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019043,
+ author = {Panagiotis Linardos and Suzanne Little and Kevin McGuinness},
+ title = {MediaEval 2019: Concealed FGSM Perturbations for Privacy Preservation.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019044,
+ author = {Hung Vinh Tran and Trong-Thang Pham and Hai-Tuan Ho-Nguyen and Hoai-Lam Nguyen-Hy and Xuan-Vy Nguyen and Thang-Long Nguyen-Ho and Minh-Triet Tran},
+ title = {HCMUS at Pixel Privacy 2019: Scene Category Protection with Back Propagation and Image Enhancement.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019045,
+ author = {Zhuoran Liu and Zhengyu Zhao},
+ title = {Adversarial Photo Frame: Concealing Sensitive Scene Information of Social Images in a User-Acceptable Manner.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019046,
+ author = {Sam Sweere and Zhuoran Liu and Martha Larson},
+ title = {Maintaining Perceptual Faithfulness of Adversarial Image Examples by Leveraging Color Variance.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019047,
+ author = {Muhammad Bilal Sakha},
+ title = {Image Enhancement and Adversarial Attack Pipeline for Scene Privacy Protection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019048,
+ author = {Mathias Lux and Michael Riegler and Duc-Tien Dang-Nguyen and Johanna Pirker and Martin Potthast and Pål Halvorsen},
+ title = {GameStory Task at MediaEval 2019.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019049,
+ author = {Van-Tu Ninh and Tu-Khiem Le and Duc-Tien Dang-Nguyen and Cathal Gurrin},
+ title = {Replay Detection and Multi-stream Synchronization in CS:GO Game Streams Using Content-Based Image Retrieval and Image Signature Matching.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019050,
+ author = {Yasufumi Moriya and Gareth J.F. Jones},
+ title = {DCU-ADAPT at MediaEval 2019: GameStory.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019051,
+ author = {Kevin Mekul},
+ title = {Automated Replay Detection and Multi-Stream Synchronization.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019052,
+ author = {Ekin Gedik and Laura Cabrera-Quiros and Hayley Hung},
+ title = {No-Audio Multimodal Speech Detection Task at MediaEval 2019.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019053,
+ author = {Panagiotis Giannakeris and Stefanos Vrochidis and Ioannis Kompatsiaris},
+ title = {Multimodal Fusion of Appearance Features, Optical Flow and Accelerometer Data for Speech Detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019054,
+ author = {Jose Vargas and Hayley Hung},
+ title = {CNNs and Fisher Vectors for No-Audio Multimodal Speech Detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019055,
+ author = {Liandong Li and Zhuo Hao and Bo Sun},
+ title = {Combining Visual and Movement Modalities for No-audio Speech Setection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019056,
+ author = {Pierre-Etienne Martin and Jenny Benois-Pineau and Boris Mansencal and Renaud Peteri and Laurent Mascarilla and Jordan Calandre and Julien Morlier},
+ title = {Sports Video Annotation: Detection of Strokes in Table Tennis Task for MediaEval 2019.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019057,
+ author = {Siddharth Sriraman and Srinath Srinivasan and Vishnu K Krishnan and Bhuvana J and T.T. Mirnalinee},
+ title = {MediaEval 2019: LRCNs for Stroke Detection in Table Tennis.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019058,
+ author = {Jordan Calandre and Renaud Péteri and Laurent Mascarilla},
+ title = {Optical Flow Singularities for Sports Video Annotation: Detection of Strokes in Table Tennis.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019059,
+ author = {Pierre-Etienne Martin and Jenny Benois-Pineau and Boris Mansencal and Renaud Péteri and Julien Morlier},
+ title = {Siamese Spatio-Temporal Convolutional Neural Network for Stroke Classification in Table Tennis Games.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019060,
+ author = {Yasufumi Moriya and Ramon Sanabria and Florian Metze and Gareth J.F. Jones},
+ title = {MediaEval 2019: Eyes and Ears Together.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019061,
+ author = {Gia-Han Diep and Duc-Tuan Luu and Son-Thanh Tran-Nguyen and Minh-Triet Tran},
+ title = {HCMUS at Eyes and Ears Together 2019: Entity Localization with Guided Word Embedding and Human Pose Estimation Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019062,
+ author = {Yasufumi Moriya and Gareth J.F. Jones},
+ title = {DCU-ADAPT at MediaEval 2019: Eyes and Ears Together.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019063,
+ author = {Yashar Deldjoo and Benny Kille and Markus Schedl and Andreas Lommatzsch and Jialie Shen},
+ title = {The 2019 Multimedia for Recommender System Task: MovieREC and NewsREEL at MediaEval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019064,
+ author = {Hossein A. Rahmani and Yashar Deldjoo and Markus Schedl},
+ title = {A Regression Approach to Movie Rating Prediction Using Multimedia Content and Metadata.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+
+@article{mediaeval2019065,
+ author = {Simon Brugman and Martha Larson},
+ title = {Scene Change Task: Take me to Paris.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2019},
+}
+@article{douradoa2019multimodal,
+ title={Multimodal Representation Model based on Graph-Based Rank Fusion},
+ author={Douradoa, Icaro Cavalcante and Tabboneb, Salvatore and da Silva Torresc, Ricardo},
+ journal={arXiv preprint arXiv:1912.10314},
+ year={2019}
+}
+@article{mediaeval00660,
+ author = {Y Wang and F Ma and Z Jin and Y Yuan and G Xun and K Jha and L Su and J Gao},
+ title = {Eann: Event adversarial neural networks for multi-modal fake news detection.},
+ journal = {ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
+ year = {2018},
+}
+@article{mediaeval00661,
+ author = {C Boididou and SE Middleton and Z Jin and S Papadopoulos and DT Dang-Nguyen and G Boato and Y Kompatsiaris},
+ title = {Verifying information with multimedia content on twitter.},
+ journal = {Multimedia Tools and Applications},
+ year = {2018},
+}
+@article{mediaeval00662,
+ author = {R Cohendet and K Yadati and NQK Duong and CH Demarty},
+ title = {Annotating, understanding, and predicting long-term video memorability.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2018},
+}
+@article{mediaeval00663,
+ author = {K Nogueira and SG Fadel and ÍC Dourado and R de O Werneck and JAV Mu{\~n}oz and OAB Penatti and RT Calumby and LT Li and JA dos Santos and R da S Torres},
+ title = {Exploiting ConvNet diversity for flooding identification.},
+ journal = {IEEE Geoscience and Remote Sensing Letters},
+ year = {2018},
+}
+@article{mediaeval00664,
+ author = {C Boididou and S Papadopoulos and M Zampoglou and L Apostolidis and O Papadopoulou and Y Kompatsiaris},
+ title = {Detection and visualization of misleading content on Twitter.},
+ journal = {International Journal of Multimedia Information Retrieval},
+ year = {2018},
+}
+@article{mediaeval00665,
+ author = {D Ram and A Asaei and H Bourlard},
+ title = {Sparse subspace modeling for query by example spoken term detection},
+ journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
+ year = {2018},
+}
+@article{mediaeval00666,
+ author = {Y Deldjoo and MG Constantin and B Ionescu and M Schedl and P Cremonesi},
+ title = {Mmtf-14k: A multifaceted movie trailer feature dataset for recommendation and retrieval},
+ journal = {ACM Multimedia Systems Conference},
+ year = {2018},
+}
+@article{mediaeval00667,
+ author = {H Squalli-Houssaini and NQK Duong and M Gwena{\"e}lle and CH Demarty},
+ title = {Deep learning for predicting image memorability.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2018},
+}
+@article{mediaeval00668,
+ author = {B Murauer and G Specht},
+ title = {Detecting Music Genre Using Extreme Gradient Boosting},
+ journal = {Companion of the The Web Conference},
+ year = {2018},
+}
+@article{mediaeval00669,
+ author = {A Erdélyi and T Winkler and B Rinner},
+ title = {Privacy protection vs. utility in visual data},
+ journal = {Multimedia Tools and Applications},
+ year = {2018},
+}
+@article{mediaeval00670,
+ author = {K Ahmad and A Sohail and N Conci and F De Natale},
+ title = {A Comparative study of Global and Deep Features for the analysis of user-generated natural disaster related images.},
+ journal = {IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
+ year = {2018},
+}
+@article{mediaeval00671,
+ author = {D Ram and L Miculicich and H Bourlard},
+ title = {CNN based query by example spoken term detection},
+ journal = {INTERSPEECH},
+ year = {2018},
+}
+@article{mediaeval00672,
+ author = {A Kirchknopf and D Slijepcevic and M Zeppelzauer and M Seidl},
+ title = {Detection of road passability from social media and satellite images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00673,
+ author = {A Moumtzidou and P Giannakeris and S Andreadis and A Mavropoulos and G Meditskos and I Gialampoukidis and K Avgerinakis and S Vrochidis and I Kompatsiaris},
+ title = {A multimodal approach in estimating road passability through a flooded area using social media and satellite images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00674,
+ author = {L Lopez-Fuentes and A Farasin and H Skinnemoen and P Garza},
+ title = {Deep learning models for passability detection in flooded roads.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00675,
+ author = {MC Madhavi and HA Patil},
+ title = {Design of mixture of GMMs for Query-by-Example spoken term detection},
+ journal = {Computer Speech and Language},
+ year = {2018},
+}
+@article{mediaeval00676,
+ author = {J Vavrek and P Viszlay and M Lojka and J Juhár and M Pleva},
+ title = {Weighted fast sequential DTW for multilingual audio Query-by-Example retrieval.},
+ journal = {Journal of Intelligent Information Systems},
+ year = {2018},
+}
+@article{mediaeval00677,
+ author = {A Lommatzsch and B Kille},
+ title = {Baseline Algorithms for Predicting the Interest in News based on Multimedia Data},
+ journal = {Proceedings of the MediaEval Workshop},
+ year = {2018},
+}
+@article{mediaeval00678,
+ author = {Y Deldjoo},
+ title = {Video recommendation by exploiting the multimedia content},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00679,
+ author = {M Larson and Z Liu and SFB Brugman and Z Zhao},
+ title = {Pixel Privacy. Increasing Image Appeal while Blocking Automatic Inference of Sensitive Scene Information},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00680,
+ author = {A Ceroni},
+ title = {Personal Photo Management and Preservation},
+ journal = {Personal Multimedia Preservation},
+ year = {2018},
+}
+@article{mediaeval00681,
+ author = {SK Singh and D Rafiei},
+ title = {Strategies for geographical scoping and improving a gazetteer},
+ journal = {ACM International Conference on World Wide Web},
+ year = {2018},
+}
+@article{mediaeval00682,
+ author = {Y Liu and Z Gu and TH Ko and KA Hua},
+ title = {Learning perceptual embeddings with two related tasks for joint predictions of media interestingness and emotions},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2018},
+}
+@article{mediaeval00683,
+ author = {N Said and K Pogorelov and K Ahmad and M Riegler and N Ahmad and O Ostroukhova and P Halvorsen and N Conci},
+ title = {Deep learning approaches for flood classification and flood aftermath detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00684,
+ author = {MS Dao and P Quang Nhat Minh and A Kasem and NS Haja Nazmudeen},
+ title = {A Context-Aware Late-Fusion Approach for Disaster Image Retrieval from Social Media},
+ journal = {International Conference on Multimedia Retrieval},
+ year = {2018},
+}
+@article{mediaeval00685,
+ author = {T Bracamonte and B Bustos and B Poblete and T Schreck},
+ title = {Extracting semantic knowledge from web context for multimedia IR: a taxonomy, survey and challenges.},
+ journal = {Multimedia Tools and Applications},
+ year = {2018},
+}
+@article{mediaeval00686,
+ author = {O Ben-Ahmed and B Huet},
+ title = {Deep Multimodal Features for Movie Genre and Interestingness Prediction},
+ journal = {IEEE International Conference on Content-Based Multimedia Indexing (CBMI)},
+ year = {2018},
+}
+@article{mediaeval00687,
+ author = {J Parekh and H Tibrewal and S Parekh},
+ title = {Deep pairwise classification and ranking for predicting media interestingness},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2018},
+}
+@article{mediaeval00688,
+ author = {G Marquant and CH Demarty and C Chamaret and J Sirot and L Chevallier},
+ title = {Interestingness Prediction and its Application to Immersive Content.},
+ journal = {IEEE International Conference on Content-Based Multimedia Indexing (CBMI)},
+ year = {2018},
+}
+@article{mediaeval00689,
+ author = {C Maigrot and V Claveau and E Kijak},
+ title = {Fusion-based multimodal detection of hoaxes in social networks},
+ journal = {IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
+ year = {2018},
+}
+@article{mediaeval00690,
+ author = {I Serrano and O Deniz and JL Espinosa-Aranda and G Bueno},
+ title = {Fight Recognition in Video Using Hough Forests and 2D Convolutional Neural Network.},
+ journal = {IEEE Transactions on Image Processing},
+ year = {2018},
+}
+@article{mediaeval00691,
+ author = {J Xue and K Eguchi},
+ title = {Supervised nonparametric multimodal topic modeling methods for multi-class video classification},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2018},
+}
+@article{mediaeval00692,
+ author = {J Xue and K Eguchi},
+ title = {Sequential Bayesian nonparametric multimodal topic models for video data analysis},
+ journal = {IEICE Transactions on Information and Systems},
+ year = {2018},
+}
+@article{mediaeval00693,
+ author = {X Zhang and Z Zhao and H Zhang and S Wang and Z Li},
+ title = {Unsupervised geographically discriminative feature learning for landmark tagging},
+ journal = {Knowledge-Based Systems},
+ year = {2018},
+}
+@article{mediaeval00694,
+ author = {G Palaiokrassas and A Voulodimos and A Litke and A Papaoikonomou and T Varvarigou},
+ title = {A Distance-Dependent Chinese Restaurant Process Based Method for Event Detection on Social Media.},
+ journal = {Inventions},
+ year = {2018},
+}
+@article{mediaeval00695,
+ author = {SFB Brugman and M Wysokinski and M Larson},
+ title = {Exploring Three Views on Image Enhancement for Pixel Privacy},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00696,
+ author = {V Thambawita and D Jha and M Riegler and P Halvorsen and HL Hammer and HD Johansen and D Johansen},
+ title = {The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00697,
+ author = {A Ciobanu and A Lommatzsch and B Kille},
+ title = {Predicting the Interest in News based On Image Annotations},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00698,
+ author = {R Cohendet and CH Demarty and NQK Duong and M Engilberge},
+ title = {VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability.},
+ journal = {arXiv preprint},
+ year = {2018},
+}
+@article{mediaeval00699,
+ author = {M Rohm and B Ionescu and AL Gînscă and RLT Santos and H M{\"u}ller},
+ title = {Subdiv17: a dataset for investigating subjectivity in the visual diversification of image search results.},
+ journal = {ACM Multimedia Systems Conference},
+ year = {2018},
+}
+@article{mediaeval00700,
+ author = {S Oramas and D Bogdanov and A Porter},
+ title = {MediaEval 2018 AcousticBrainz Genre Task: A baseline combining deep feature embeddings across datasets},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00701,
+ author = {S Nadeem and MA Tahir and SSA Naqvi and M Zaid},
+ title = {Ensemble of Texture and Deep Learning Features for Finding Abnormalities in the Gastro-Intestinal Tract},
+ journal = {International Conference on Computational Collective Intelligence},
+ year = {2018},
+}
+@article{mediaeval00702,
+ author = {M Taschwer and MJ Primus and K Schoeffmann and O Marques},
+ title = {Early and Late Fusion of Classifiers for the MediaEval Medico Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00703,
+ author = {AF Smeaton and O Corrigan and P Dockree and C Gurrin and G Healy and F Hu and K McGuinness and E Mohedano and TE Ward},
+ title = {Dublin's participation in the predicting media memorability task at MediaEval 2018.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00704,
+ author = {P Galuščáková},
+ title = {Information retrieval and navigation in audio-visual archives},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00705,
+ author = {J Cao},
+ title = {Large-scale content management and retrieval for social media},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00706,
+ author = {Y Yi and H Wang},
+ title = {Multi-modal learning for affective content analysis in movies},
+ journal = {Multimedia Tools and Applications},
+ year = {2018},
+}
+@article{mediaeval00707,
+ author = {Z Zhao and MA Larson and NHJ Oostdijk},
+ title = {Exploiting Local Semantic Concepts for Flooding-related Social Image Classification},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018},
+}
+@article{mediaeval00708,
+ author = {S Sarman and M Sert},
+ title = {Audio based violent scene classification using ensemble learning},
+ journal = {IEEE International Symposium on Digital Forensic and Security (ISDFS)},
+ year = {2018},
+}
+@article{mediaeval00709,
+ author = {RDO Werneck and IC Dourado and SG Fadel and S Tabbone and RdaS Torres},
+ title = {Graph-Based Early-Fusion for Flood Detection.},
+ journal = {IEEE International Conference on Image Processing (ICIP)},
+ year = {2018},
+}
+@article{mediaeval00710,
+ author = {X Cheng and X Zhang and M Xu and TF Zheng},
+ title = {MMANN: Multimodal Multilevel Attention Neural Network for Horror Clip Detection.},
+ journal = {IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},
+ year = {2018},
+}
+@article{mediaeval00711,
+ author = {TJ Bracamonte Nole},
+ title = {Improving web multimedia information retrieval using social data},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00712,
+ author = {X Zhang and X Cheng and M Xu and TF Zheng},
+ title = {Imbalance Learning-based Framework for Fear Recognition in the MediaEval Emotional Impact of Movies Task},
+ journal = {Interspeech},
+ year = {2018},
+}
+@article{mediaeval00713,
+ author = {MR Khokher},
+ title = {Audio-visual Video Recognition Through Super Descriptor Tensor Decomposition and Low-rank and Sparse Representation},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00714,
+ author = {B Zou and M Nurudeen and C Zhu and Z Zhang and R Zhao and L Wang},
+ title = {A Neuro-Fuzzy Crime Prediction Model Based on Video Analysis},
+ journal = {Chinese Journal of Electronics},
+ year = {2018},
+}
+@article{mediaeval00715,
+ author = {S Wang and S Chen and J Zhao and Q Jin},
+ title = {Video Interestingness Prediction Based on Ranking Model},
+ journal = {ACM Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and first Multi-Modal Affective Computing of Large-Scale Multimedia Data},
+ year = {2018},
+}
+@article{mediaeval00716,
+ author = {AM Atto and A Benoît and P Lambert},
+ title = {Hilbert Based Video To Timed-Image Filling and Learning To Recognize Visual Violence},
+ year = {2018},
+}
+@article{mediaeval00717,
+ author = {C Maigrot and E Kijak and V Claveau},
+ title = {Fusion par apprentissage pour la détection de fausses informations dans les réseaux sociaux},
+ journal = {Document numerique},
+ year = {2018},
+}
+@article{mediaeval00718,
+ author = {Y Timar and N Karslioglu and H Kaya and AA Salah},
+ title = {Feature Selection and Multimodal Fusion for Estimating Emotions Evoked by Movie Clips},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2018},
+}
+@article{mediaeval00719,
+ author = {M Muszynski},
+ title = {Recognizing film aesthetics, spectators' affect and aesthetic emotions from multimodal signals},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00720,
+ author = {C Hou and X Wu and G Wang},
+ title = {End-to-End Bloody Video Recognition by Audio-Visual Feature Fusion},
+ journal = {Chinese Conference on Pattern Recognition and Computer Vision},
+ year = {2018},
+}
+@article{mediaeval00721,
+ author = {NR Nandigam},
+ title = {MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00722,
+ author = {A Khwileh},
+ title = {Towards effective cross-lingual search of user-generated internet speech},
+ journal = {PhD Thesis},
+ year = {2018},
+}
+@article{mediaeval00001,
+ author = {RJ Borgli and P Halvorsen and M Riegler and HK Stensland},
+ title = {Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00002,
+ author = {M Lux and M Riegler and DT Dang-Nguyen and M Larson and M Potthast and P Halvorsen},
+ title = {Team ORG@ GameStory Task 2018.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00007,
+ author = {S Wang and W Wang and S Chen and Q Jin},
+ title = {RUC at MediaEval 2018: Visual and Textual Features Exploration for Predicting Media Memorability},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00003,
+ author = {M Wutti},
+ title = {Automated Killstreak Extraction in CS: GO Tournaments},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00004,
+ author = {R Gupta and K Motwani},
+ title = {Linear Models for Video Memorability Prediction Using Visual and Semantic Features},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00006,
+ author = {Y Liu and Z Gu and TH Ko},
+ title = {Analyzing Human Behavior in Subspace: Dimensionality Reduction+ Classification},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00008,
+ author = {L Cabrera-Quiros and E Gedik and H Hung},
+ title = {Transductive Parameter Transfer, Bags of Dense Trajectories and MILES for No-Audio Multimodal Speech Detection},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00009,
+ author = {SA Hicks and PH Smedsrud and P Halvorsen and M Riegler},
+ title = {Deep Learning Based Disease Detection Using Domain Specific Transfer Learning},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00011,
+ author = {TH Ko and Z Gu and Y Liu},
+ title = {Weighted Discriminant Embedding: Discriminant Subspace Learning for Imbalanced Medical Data Classification},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00012,
+ author = {W Sun and X Zhang},
+ title = {Video Memorability Prediction with Recurrent Neural Networks and Video Titles at the 2018 MediaEval Predicting Media Memorability Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00013,
+ author = {F Nazary and Y Deldjoo},
+ title = {Movie Rating Prediction using Multimedia Content and Modeling as a Classification Problem},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00014,
+ author = {T Joshi and S Sivaprasad and S Bhat and N Pedanekar},
+ title = {Multimodal Approach to Predicting Media Memorability},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00015,
+ author = {Z Khan and MA Tahir},
+ title = {Majority voting of Heterogeneous Classifiers for finding abnormalities in the Gastro-Intestinal Tract},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00016,
+ author = {H Schreiber},
+ title = {MediaEval 2018 AcousticBrainz Genre Task: A CNN Baseline Relying on Mel-Features},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00017,
+ author = {D Dias and U Dias},
+ title = {Transfer learning with CNN architectures for classifying gastrointestinal diseases and anatomical landmarks},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00018,
+ author = {O Ostroukhova and K Pogorelov and M Riegler and DT Dang-Nguyen and P Halvorsen},
+ title = {Transfer learning with prioritized classification and training dataset equalization for medical objects detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00019,
+ author = {Y Liu and Z Gu and TH Ko},
+ title = {Learning Memorability Preserving Subspace for Predicting Media Memorability},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00020,
+ author = {DT Tran-Van and LV Tran and MT Tran},
+ title = {Predicting Media Memorability Using Deep Features and Recurrent Network},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00021,
+ author = {A Weiss and B Sang and S Yoon},
+ title = {Predicting Memorability via Early Fusion Deep Neural Network},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00022,
+ author = {RM Savii and SF dos Santos and J Almeida},
+ title = {GIBIS at MediaEval 2018: Predicting Media Memorability Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00023,
+ author = {R Chaudhry and M Kilaru and S Shekhar},
+ title = {Show and Recall@ MediaEval 2018 ViMemNet: Predicting Video Memorability},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00024,
+ author = {R Cohendet and CH Demarty and NQK Duong},
+ title = {Transfer learning for video memorability prediction},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00026,
+ author = {TH Ko and Z Gu and T He and Y Liu},
+ title = {Towards Learning Emotional Subspace},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00027,
+ author = {C Loughridge and J Moseyko},
+ title = {IM-JAIC at MediaEval 2018 Emotional Impact of Movies Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00029,
+ author = {JJ Sun and T Liu and G Prasad},
+ title = {GLA in MediaEval 2018 Emotional Impact of Movies Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00030,
+ author = {M Hanif and MA Tahir and M Rafi},
+ title = {Detection of passable roads using Ensemble of Global and Local},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00031,
+ author = {P Lopez-Otero and L Docio-Fernandez},
+ title = {Converted Mel-Cepstral Coefficients for Gender Variability Reduction in Query-by-Example Spoken Document Retrieval},
+ journal = {isca-speech.org},
+ year = {2018}
+}
+@article{mediaeval00032,
+ author = {E Batziou and E Michail and K Avgerinakis and S Vrochidis and I Patras and I Kompatsiaris},
+ title = {Visual and audio analysis of movies video for emotion detection@ Emotional Impact of Movies task MediaEval 2018.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00033,
+ author = {D Dias and U Dias},
+ title = {Flood detection from social multimedia and satellite images using ensemble and transfer learning with CNN architectures},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00034,
+ author = {B Bischke and P Helber and A Dengel},
+ title = {Global-Local Feature Fusion for Image Classification of Flood Affected Roads from Social Multimedia},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00039,
+ author = {Y Yi and H Wang and Q Li},
+ title = {CNN Features for Emotional Impact of Movies Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00041,
+ author = {YM Yang},
+ title = {Short-time emotion tracker using a convolutional autoencoder},
+ year = {2018}
+}
+@article{mediaeval00035,
+ author = {KAC Quan and VT Nguyen and MT Tran},
+ title = {Frame-based Evaluation with Deep Features to Predict Emotional Impact of Movies},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2018}
+}
+@article{mediaeval00052,
+ author = {Y Feng and S Shebotnov and C Brenner and M Sester},
+ title = {Ensembled convolutional neural network models for retrieving flood relevant tweets},
+ journal = {Image},
+ year = {2018},
+}
+
+@inproceedings{ramirez2018overview,
+ title={Overview of the multimedia information processing for personality \& social networks analysis contest},
+ author={G Ram{\'\i}rez and E Villatoro and B Ionescu and HJ Escalante and S Escalera and M Larson and H. M{\"u}ller and I. Guyon},
+ booktitle={International Conference on Pattern Recognition},
+ pages={127--139},
+ year={2018},
+ organization={Springer}
+}
+
+@article{mediaeval00553,
+ author = {B Bischke and P Bhardwaj and A Gautam and P Helber and D Borth and A Dengel},
+ title = {Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00554,
+ author = {Z Jin and J Cao and H Guo and Y Zhang and J Luo},
+ title = {Multimodal fusion with recurrent neural networks for rumor detection on microblogs},
+ journal = {ACM International conference on Multimedia},
+ year = {2017},
+}
+@article{mediaeval00555,
+ author = {A Aljanaki and YH Yang and M Soleymani},
+ title = {Developing a benchmark for emotional analysis of music},
+ journal = {PloS one},
+ year = {2017},
+}
+@article{mediaeval00556,
+ author = {Y Baveye and C Chamaret and E Dellandréa and L Chen},
+ title = {Affective video content analysis: A multidisciplinary insight},
+ journal = {IEEE Transactions on Affective Computing},
+ year = {2017},
+}
+@article{mediaeval00557,
+ author = {C Boididou and S Papadopoulos and L Apostolidis and Y Kompatsiaris},
+ title = {Learning to detect misleading content on twitter.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2017},
+}
+@article{mediaeval00558,
+ author = {K Avgerinakis and A Moumtzidou and S Andreadis and E Michail and I Gialampoukidis and S Vrochidis and I Kompatsiaris},
+ title = {Visual and Textual Analysis of Social Media and Satellite Images for Flood Detection@ Multimedia Satellite Task MediaEval 2017.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00051,
+ author = {C Raymond and G Gravier},
+ title = {Deep Neural Architectures for Automatic Representation Learning from Multimedia Multimodal Data},
+ journal = {PhD Thesis},
+ year = {2017}
+}
+@article{mediaeval00559,
+ author = {Z Yang and Q Li and W Liu and Y Ma and M Cheng},
+ title = {Dual graph regularized NMF model for social event detection from Flickr data},
+ journal = {ACM International Conference on World Wide Web},
+ year = {2017},
+}
+@article{mediaeval00560,
+ author = {K Ahmad and K Pogorelov and M Riegler and N Conci and P Halvorsen},
+ title = {CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00561,
+ author = {X Hu and JH Lee and D Bainbridge and K Choi and P Organisciak and JS Downie},
+ title = {The MIREX grand challenge: A framework of holistic user-experience evaluation in music information retrieval.},
+ journal = {Journal of the Association for Information Science and Technology},
+ year = {2017},
+}
+@article{mediaeval00562,
+ author = {DT Dang-Nguyen and L Piras and G Giacinto and G Boato and FGB De Natale},
+ title = {Multimodal retrieval with diversification and relevance feedback for tourist attraction images.},
+ journal = {ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
+ year = {2017},
+}
+@article{mediaeval00563,
+ author = {A Lidon and M Bola{\~n}os and M Dimiccoli and P Radeva and M Garolera and X Giro-i-Nieto},
+ title = {Semantic summarization of egocentric photo stream events.},
+ journal = {Workshop on Lifelogging Tools and Applications},
+ year = {2017},
+}
+@article{mediaeval00564,
+ author = {G Kordopatis-Zilos and S Papadopoulos and I Kompatsiaris},
+ title = {Geotagging text content with language models and feature mining.},
+ journal = {Proceedings of the IEEE},
+ year = {2017},
+}
+@article{mediaeval00565,
+ author = {M Malik and S Adavanne and K Drossos and T Virtanen and D Ticha and R Jarina},
+ title = {Stacked convolutional and recurrent neural networks for music emotion recognition.},
+ journal = {arXiv preprint},
+ year = {2017},
+}
+@article{mediaeval00566,
+ author = {U Ahsan and C Sun and J Hays and I Essa},
+ title = {Complex event recognition from images with few training examples},
+ journal = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
+ year = {2017},
+}
+@article{mediaeval00567,
+ author = {Y Liu and Z Gu and Y Cheung and KA Hua},
+ title = {Multi-view manifold learning for media interestingness prediction},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2017},
+}
+@article{mediaeval00568,
+ author = {Z Yang and Q Li and Z Lu and Y Ma and Z Gong and W Liu},
+ title = {Dual structure constrained multimodal feature coding for social event detection from Flickr data},
+ journal = {ACM Transactions on Internet Technology (TOIT)},
+ year = {2017},
+}
+@article{mediaeval00569,
+ author = {MC Madhavi and HA Patil},
+ title = {Partial matching and search space reduction for QbE-STD},
+ journal = {Computer Speech and Language},
+ year = {2017},
+}
+@article{mediaeval00570,
+ author = {D Won and ZC Steinert-Threlkeld and J Joo},
+ title = {Protest activity detection and perceived violence estimation from social media images},
+ journal = {ACM international conference on Multimedia},
+ year = {2017},
+}
+@article{mediaeval00571,
+ author = {Z Jin and Y Yao and Y Ma and M Xu},
+ title = {THUHCSI in MediaEval 2017 Emotional Impact of Movies Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00572,
+ author = {M Zaharieva and B Ionescu and AL Gînsca and RLT Santos and H M{\"u}ller},
+ title = {Retrieving Diverse Social Images at MediaEval 2017: Challenges, Dataset and Evaluation.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00573,
+ author = {G Petkos and M Schinas and S Papadopoulos and Y Kompatsiaris},
+ title = {Graph-based multimodal clustering for social multimedia.},
+ journal = {Multimedia Tools and Applications},
+ year = {2017},
+}
+@article{mediaeval00574,
+ author = {CH Demarty and M Sjöberg and MG Constantin and NQK Duong and B Ionescu and TT Do and H Wang},
+ title = {Predicting interestingness of visual content.},
+ journal = {Visual Content Indexing and Retrieval with Psycho-Visual Models},
+ year = {2017},
+}
+@article{mediaeval00575,
+ author = {J Choi and M Larson and X Li and K Li and G Friedland and A Hanjalic},
+ title = {The Geo-Privacy Bonus of Popular Photo Enhancements.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2017},
+}
+@article{mediaeval00576,
+ author = {D Moreira and S Avila and M Perez and D Moraes and V Testoni and E Valle and S Goldenstein and A Rocha},
+ title = {Temporal robust features for violence detection.},
+ journal = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
+ year = {2017},
+}
+@article{mediaeval00577,
+ author = {S Ahmad and K Ahmad and N Ahmad and N Conci},
+ title = {Convolutional Neural Networks for Disaster Images Retrieval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00578,
+ author = {L Tian and M Muszynski and C Lai and JD Moore and T Kostoulas and P Lombardo and T Pun and G Chanel},
+ title = {Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?},
+ journal = {IEEE International Conference on Affective Computing and Intelligent Interaction (ACII)},
+ year = {2017},
+}
+@article{mediaeval00579,
+ author = {H Chen and CC Leung and L Xie and B Ma and H Li},
+ title = {Multitask feature learning for low-resource query-by-example spoken term detection.},
+ journal = {IEEE Journal of Selected Topics in Signal Processing},
+ year = {2017},
+}
+@article{mediaeval00580,
+ author = {MG Constantin and BA Boteanu and B Ionescu},
+ title = {LAPI at MediaEval 2017-Predicting Media Interestingness.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00581,
+ author = {B Gündoğdu and B Yusuf and M Saraçlar},
+ title = {Joint learning of distance metric and query model for posteriorgram-based keyword search},
+ journal = {IEEE Journal of Selected Topics in Signal Processing},
+ year = {2017},
+}
+@article{mediaeval00582,
+ author = {E Coutinho and B Schuller},
+ title = {Shared acoustic codes underlie emotional communication in music and speech - Evidence from deep transfer learning},
+ journal = {PloS one},
+ year = {2017},
+}
+@article{mediaeval00583,
+ author = {MC Madhavi and HA Patil},
+ title = {VTLN-warped Gaussian posteriorgram for QbE-STD},
+ journal = {IEEE European Signal Processing Conference (EUSIPCO)},
+ year = {2017},
+}
+@article{mediaeval00584,
+ author = {X Li and M Larson and A Hanjalic},
+ title = {Geo-distinctive visual element matching for location estimation of images},
+ journal = {IEEE Transactions on Multimedia},
+ year = {2017},
+}
+@article{mediaeval00585,
+ author = {E Sansone and K Apostolidis and N Conci and G Boato and V Mezaris and FGB De Natale},
+ title = {Automatic synchronization of multi-user photo galleries.},
+ journal = {IEEE Transactions on Multimedia},
+ year = {2017},
+}
+@article{mediaeval00586,
+ author = {N Tkachenko and A Zubiaga and R Procter},
+ title = {Wisc at mediaeval 2017: Multimedia satellite task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00587,
+ author = {Q Gan and S Wang and L Hao and Q Ji},
+ title = {A multimodal deep regression bayesian network for affective video content analyses},
+ journal = {IEEE International Conference on Computer Vision},
+ year = {2017},
+}
+@article{mediaeval00588,
+ author = {I Gialampoukidis and A Moumtzidou and D Liparas and T Tsikrika and S Vrochidis and I Kompatsiaris},
+ title = {Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression.},
+ journal = {Multimedia Tools and Applications},
+ year = {2017},
+}
+@article{mediaeval00589,
+ author = {X Zhang and S Wang and Z Li and S Ma},
+ title = {Landmark image retrieval by jointing feature refinement and multimodal classifier learning},
+ journal = {IEEE transactions on cybernetics},
+ year = {2017},
+}
+@article{mediaeval00590,
+ author = {K Pogorelov and M Riegler and P Halvorsen and C Griwodz and T de Lange and K Randel and S Eskeland and D Nguyen and D Tien and O Ostroukhova and others},
+ title = {A comparison of deep learning with global features for gastrointestinal disease detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00591,
+ author = {N Le and H Bredin and G Sargent and P Lopez-Otero and C Barras and C Guinaudeau and G Gravier and GB da Fonseca and IL Freire and others},
+ title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news.},
+ journal = {ACM International Workshop on Content-Based Multimedia Indexing},
+ year = {2017},
+}
+@article{mediaeval00592,
+ author = {X Fu and Y Bin and L Peng and J Zhou and Y Yang and HT Shen},
+ title = {BMC@ MediaEval 2017 Multimedia Satellite Task via Regression Random Forest.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00593,
+ author = {E Berson and CH Demarty and N Duong},
+ title = {Multimodality and deep learning when predicting media interestingness.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00594,
+ author = {J Parekh and H Tibrewal and S Parekh},
+ title = {The IITB Predicting Media Interestingness System for MediaEval 2017.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00595,
+ author = {O Papadopoulou and M Zampoglou and S Papadopoulos and Y Kompatsiaris},
+ title = {Web video verification using contextual cues.},
+ journal = {ACM International Workshop on Multimedia Forensics and Security},
+ year = {2017},
+}
+@article{mediaeval00596,
+ author = {Y Liu and Z Gu and TH Ko},
+ title = {Predicting Media Interestingness via Biased Discriminant Embedding and Supervised Manifold Regression.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00597,
+ author = {Y Shen and CH Demarty and NQK Duong},
+ title = {Deep learning for multimodal-based video interestingness prediction.},
+ journal = {IEEE International Conference on Multimedia and Expo (ICME)},
+ year = {2017},
+}
+@article{mediaeval00598,
+ author = {RFE Sutcliffe and Ó Maidín},
+ title = {… C@merata task at MediaEval 2017: Natural Language Queries about Music, their JSON Representations, and Matching Passages in MusicXML Scores},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00599,
+ author = {S Petscharnig and K Schöffmann and M Lux},
+ title = {An Inception-like CNN Architecture for GI Disease and Anatomical Landmark Classification.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00600,
+ author = {K Koutini and A Imenina and M Dorfer and A Gruber and M Schedl},
+ title = {MediaEval 2017 AcousticBrainz Genre Task: Multilayer Perceptron Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00601,
+ author = {B Murauer and M Mayerl and M Tschuggnall and E Zangerle and M Pichl and G Specht},
+ title = {Hierarchical Multilabel Classification and Voting for Genre Classification.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00602,
+ author = {K Nogueira and SG Fadel and ÍC Dourado and R de Oliveira Werneck and JAV Mu{\~n}oz and OAB Penatti and RT Calumby and L Li and JA dos Santos and R da Silva Torres},
+ title = {Data-Driven Flood Detection using Neural Networks.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00603,
+ author = {B Gündogdu},
+ title = {Keyword search for low resource languages},
+ year = {2017},
+}
+@article{mediaeval00604,
+ author = {Z Beiji and N Mohammed and Z Chengzhang and Z Rongchang},
+ title = {Crime hotspot detection and monitoring using video based event modeling and mapping techniques.},
+ journal = {International Journal of Computational Intelligence Systems},
+ year = {2017},
+}
+@article{mediaeval00605,
+ author = {J Almeida and LP Valem and DCG Pedronette},
+ title = {A rank aggregation framework for video interestingness prediction},
+ journal = {International Conference on Image Analysis and Processing},
+ year = {2017},
+}
+@article{mediaeval00606,
+ author = {MG Constantin and B Ionescu},
+ title = {Content description for Predicting image Interestingness},
+ journal = {IEEE International Symposium on Signals, Circuits and Systems (ISSCS)},
+ year = {2017},
+}
+@article{mediaeval00607,
+ author = {R Chakraborty and M Pandharipande and SK Kopparapu},
+ title = {Analyzing Emotion in Spontaneous Speech},
+ year = {2017},
+}
+@article{mediaeval00608,
+ author = {MC Madhavi and HA Patil},
+ title = {Combining evidences from detection sources for query-by-example spoken term detection},
+ journal = {IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},
+ year = {2017},
+}
+@article{mediaeval00609,
+ author = {P Galuščáková and M Batko and J Čech and J Matas and D Nov{\'a}k and P Pecina},
+ title = {Visual descriptors in methods for video hyperlinking.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2017},
+}
+@article{mediaeval00610,
+ author = {RFE Sutcliffe and DÓ Maidín and EH Hovy},
+ title = {The C@merata task at MediaEval 2017: Natural Language Queries about Music, their JSON Representations, and Matching Passages in MusicXML Scores.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00611,
+ author = {T Agrawal and R Gupta and S Sahu and CY Espy-Wilson},
+ title = {SCL-UMD at the Medico Task-MediaEval 2017: Transfer Learning based Classification of Medical Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00612,
+ author = {Y Liu and Z Gu and WK Cheung},
+ title = {HKBU at MediaEval 2017 Medico: Medical multimedia task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00613,
+ author = {S Yoon},
+ title = {TCNJ-CS@ MediaEval 2017 Emotional Impact of Movie Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00614,
+ author = {M Rueben and WD Smart and CM Grimm and M Cakmak},
+ title = {Privacy-Sensitive Robotics.},
+ journal = {ACM/IEEE International Conference on Human-Robot Interaction},
+ year = {2017},
+}
+@article{mediaeval00615,
+ author = {L Tian and JD Moore and C Lai},
+ title = {Recognizing emotions in spoken dialogue with acoustic and lexical cues},
+ journal = {Proceedings of the international ACM SIGIR},
+ year = {2017},
+}
+@article{mediaeval00616,
+ author = {P Lopez-Otero and LD Fernández and C Garcia-Mateo},
+ title = {Compensating Gender Variability in Query-by-Example Search on Speech Using Voice Conversion.},
+ journal = {INTERSPEECH},
+ year = {2017},
+}
+@article{mediaeval00617,
+ author = {C Baecchi and T Uricchio and M Bertini and A Del Bimbo},
+ title = {Deep sentiment features of context and faces for affective video analysis},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2017},
+}
+@article{mediaeval00618,
+ author = {S Baruah and R Gupta and S Narayanan},
+ title = {A knowledge transfer and boosting approach to the prediction of affect in movies},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2017},
+}
+@article{mediaeval00619,
+ author = {B Wang and M Larson},
+ title = {Beyond concept detection: The potential of user intent for image retrieval},
+ journal = {ACM Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes},
+ year = {2017},
+}
+@article{mediaeval00620,
+ author = {A Katsiavalos},
+ title = {The DMUN System at the MediaEval 2017 C@merata Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00621,
+ author = {JM Renders and G Csurka},
+ title = {NLE@ MediaEval'17: Combining Cross-Media Similarity and Embeddings for Retrieving Diverse Social Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00622,
+ author = {AM Abdelhalim and MAM Salem},
+ title = {Intelligent organization of multiuser photo galleries using sub-event detection},
+ journal = {IEEE International Conference on Computer Engineering and Systems (ICCES)},
+ year = {2017},
+}
+@article{mediaeval00623,
+ author = {N Karslioglu and Y Timar and AA Salah and H Kaya},
+ title = {BOUN-NKU in MediaEval 2017 Emotional Impact of Movies Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00624,
+ author = {Z Zhao and M Larson},
+ title = {Retrieving Social Flooding Images Based on Multimodal Information},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00625,
+ author = {T Chen and Y Wang and S Wang and S Chen},
+ title = {Exploring Domain Knowledge for Affective Video Content Analyses},
+ journal = {ACM international conference on Multimedia},
+ year = {2017},
+}
+@article{mediaeval00626,
+ author = {H Wang and L Yang and X Wu and J He},
+ title = {A review of bloody violence in video classification},
+ journal = {IEEE International Conference on the Frontiers and Advances in Data Science (FADS)},
+ year = {2017},
+}
+@article{mediaeval00627,
+ author = {RA Permadi and SGP Putra and C Helmiriawan and CCS Liem},
+ title = {DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00628,
+ author = {J Almeida and RM Savii},
+ title = {GIBIS at MediaEval 2017: Predicting Media Interestingness Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00629,
+ author = {Y Ma and X Li and M Xu and J Jia and L Cai},
+ title = {Multi-scale context based attention for dynamic music emotion prediction},
+ journal = {ACM international conference on Multimedia},
+ year = {2017},
+}
+@article{mediaeval00630,
+ author = {A Jan},
+ title = {Deep learning based facial expression recognition and its applications},
+ year = {2017},
+}
+@article{mediaeval00631,
+ author = {B Boteanu and I. Mironică and B Ionescu},
+ title = {Pseudo-relevance feedback diversification of social image retrieval results},
+ journal = {Multimedia Tools and Applications},
+ year = {2017},
+}
+@article{mediaeval00632,
+ author = {N Bouhlel and G Feki and AB Ammar and CB Amar},
+ title = {A hypergraph-based reranking model for retrieving diverse social images},
+ journal = {International Conference on Computer Analysis of Images and Patterns},
+ year = {2017},
+}
+@article{mediaeval00633,
+ author = {L Cardoner Campi},
+ title = {Deep learning for multimedia processing-Predicting media interestingness},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00634,
+ author = {O Seddati and N Ben-Lhachemi and S Dupont and S Mahmoudi},
+ title = {UMONS@ MediaEval 2017: Diverse Social Images Retrieval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00635,
+ author = {RT Calumby and IBA do Carmo Araujo and FS Cordeiro and F Bertoni and SD Canuto and F Bel{\'e}m and MA Gon{\c{c}}alves and IC Dourado and J Munoz and L Li and others},
+ title = {Rank Fusion and Multimodal Per-topic Adaptiveness for Diverse Image Retrieval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00636,
+ author = {N Dauban},
+ title = {DNN in the AcousticBrainz Genre Task 2017.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00637,
+ author = {K Kim and J Choi},
+ title = {ICSI in MediaEval 2017 Multi-Genre Music Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00638,
+ author = {L Peng and Y Bin and X Fu and J Zhou and Y Yang and HT Shen},
+ title = {CFM@ MediaEval 2017 Retrieving Diverse Social Images Task via Re-ranking and Hierarchical Clustering.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00639,
+ author = {B Wang and M Larson},
+ title = {Exploiting visual-based intent classifcation for Diverse Social Image Retrieval},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2017},
+}
+@article{mediaeval00640,
+ author = {GB Da Fonseca and IL Freire and Z Patrocínio Jr and SJF Guimar{\~a}es and G Sargent and R Sicre and G Gravier},
+ title = {Tag Propagation Approaches within Speaking Face Graphs for Multimodal Person Discovery.},
+ journal = {ACM International Workshop on Content-Based Multimedia Indexing},
+ year = {2017},
+}
+@article{mediaeval00641,
+ author = {D Rodríguez Navarro},
+ title = {Multimodal Deep Learning methods for person annotation in video sequences},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00642,
+ author = {J Zahálka},
+ title = {The machine in multimedia analytics},
+ year = {2017},
+}
+@article{mediaeval00643,
+ author = {D Ferrés Domènech},
+ title = {Knowledge-based and data-driven approaches for geographical information access},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00644,
+ author = {E Celik},
+ title = {Affective analysis of videos: detecting emotional content in real-life scenarios.},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00646,
+ author = {F Qi and X Yang and T Zhang and C Xu},
+ title = {Discriminative Multimodal Embedding for Event Classication},
+ year = {2017},
+}
+@article{mediaeval00647,
+ author = {T Agrawal and R Gupta and S Narayanan},
+ title = {Multimodal detection of fake social media use through a fusion of classification and pairwise ranking systems.},
+ journal = {IEEE European Signal Processing Conference (EUSIPCO)},
+ year = {2017},
+}
+@article{mediaeval00648,
+ author = {M Truelove},
+ title = {Witnesses of events in social media.},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00649,
+ author = {TP Thomas},
+ title = {The Emotional Impact of Audio-Visual Stimuli},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00650,
+ author = {T Thomas and M Domínguez and R Ptucha},
+ title = {Deep independent audio-visual affect analysis.},
+ journal = {IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
+ year = {2017},
+}
+@article{mediaeval00651,
+ author = {J Xue and K Eguchi},
+ title = {Video Data Modeling Using Sequential Correspondence Hierarchical Dirichlet Processes},
+ journal = {IEICE TRANSACTIONS on Information and Systems},
+ year = {2017},
+}
+@article{mediaeval00652,
+ author = {S Xiang and W Rong and Z Xiong and M Gao and Q Xiong},
+ title = {Visual and Audio Aware Bi-Modal Video Emotion Recognition.},
+ journal = {CogSci},
+ year = {2017},
+}
+@article{mediaeval00653,
+ author = {J White and DW Oard},
+ title = {Simulating Zero-Resource Spoken Term Discovery},
+ journal = {ACM on Conference on Information and Knowledge Management},
+ year = {2017},
+}
+@article{mediaeval00654,
+ author = {J van Hout and V Mitra and H Franco and C Bartels and D Vergyri},
+ title = {Tackling unseen acoustic conditions in query-by-example search using time and frequency convolution for multilingual deep bottleneck features.},
+ journal = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
+ year = {2017},
+}
+@article{mediaeval00655,
+ author = {N Alsaedi},
+ title = {Event identification in social media using classification-clustering framework.},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00656,
+ author = {P Raghavendra Reddy and K Sri Rama Murty and B Yegnanarayana},
+ title = {Representation Learning for Spoken Term Detection.},
+ journal = {Pattern recognition and big data},
+ year = {2017},
+}
+@article{mediaeval00657,
+ author = {MC Madhavi and HA Patil},
+ title = {Two Stage Zero-resource Approaches for QbE-STD},
+ journal = {IEEE International Conference on Advances in Pattern Recognition (ICAPR)},
+ year = {2017},
+}
+@article{mediaeval00658,
+ author = {V Vukotic},
+ title = {Deep neural architectures for automatic representation learning from multimedia multimodal data},
+ journal = {PhD Thesis},
+ year = {2017},
+}
+@article{mediaeval00468,
+ author = {E Dellandréa and L Chen and Y Baveye and M Sj{\"o}berg and C Chamaret},
+ title = {The mediaeval 2016 emotional impact of movies task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00469,
+ author = {N Panagiotou and I Katakis and D Gunopulos},
+ title = {Detecting events in online social networks: Definitions, trends and challenges},
+ journal = {Solving Large Scale Learning Tasks. Challenges and Algorithms},
+ year = {2016},
+}
+@article{mediaeval00470,
+ author = {I. Mironică and IC Duta and B Ionescu and N Sebe},
+ title = {A modified vector of locally aggregated descriptors approach for fast video classification},
+ journal = {Multimedia Tools and Applications},
+ year = {2016},
+}
+@article{mediaeval00471,
+ author = {S Papadopoulos and K Bontcheva and E Jaho and M Lupu and C Castillo},
+ title = {Overview of the special issue on trust and veracity of information in social media.},
+ journal = {ACM Transactions on Information Systems (TOIS)},
+ year = {2016},
+}
+@article{mediaeval00472,
+ author = {T Kaneko and K Yanai},
+ title = {Event photo mining from twitter using keyword bursts and image clustering},
+ journal = {Neurocomputing},
+ year = {2016},
+}
+@article{mediaeval00473,
+ author = {B Ionescu and A Popescu and AL Radu and H Muller},
+ title = {Result diversification in social image retrieval: a benchmarking framework},
+ journal = {Multimedia Tools and Applications},
+ year = {2016},
+}
+@article{mediaeval00474,
+ author = {V Vukotić and C Raymond and G Gravier},
+ title = {Bidirectional joint representation learning with symmetrical deep neural networks for multimodal and crossmodal applications.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2016},
+}
+@article{mediaeval00475,
+ author = {PC Ribeiro and R Audigier and QC Pham},
+ title = {RIMOC, a feature to discriminate unstructured motions: Application to violence detection for video-surveillance},
+ journal = {Computer vision and image understanding},
+ year = {2016},
+}
+@article{mediaeval00476,
+ author = {P Lopez-Otero and L Docio-Fernandez and C Garcia-Mateo},
+ title = {Finding relevant features for zero-resource query-by-example search on speech.},
+ journal = {Speech Communication},
+ year = {2016},
+}
+@article{mediaeval00477,
+ author = {Y Shen and CH Demarty and NQK Duong},
+ title = {Technicolor@ MediaEval 2016 Predicting Media Interestingness Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00478,
+ author = {G Boato and DT Dang-Nguyen and O Muratov and N Alajlan and FGB De Natale},
+ title = {Exploiting visual saliency for increasing diversity of image retrieval results.},
+ journal = {Multimedia Tools and Applications},
+ year = {2016},
+}
+@article{mediaeval00479,
+ author = {I. Mironică and B Ionescu and J Uijlings and N Sebe},
+ title = {Fisher kernel temporal variation-based relevance feedback for video retrieval},
+ journal = {Computer vision and image understanding},
+ year = {2016},
}
-@article{mediaeval00318,
- author = {C Guinaudeau and A Laurent and H Bredin},
- title = {LIMSI@ MediaEval SED 2014.},
+@article{mediaeval00480,
+ author = {H Xu and J Hou and X Xiao and CC Leung and L Wang and H Lv and L Xie and B Ma and ES Chng and H Li and others},
+ title = {Approximate search of audio queries by using DTW with phone time boundary and data augmentation.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2016},
+}
+@article{mediaeval00481,
+ author = {H Bredin and C Barras and C Guinaudeau},
+ title = {Multimodal person discovery in broadcast TV at MediaEval 2016},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00482,
+ author = {Y Ma and Z Ye and M Xu},
+ title = {THU-HCSI at MediaEval 2016: Emotional Impact of Movies Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00483,
+ author = {CC Leung and L Wang and H Xu and J Hou and ...},
+ title = {Toward High-Performance Language-Independent Query-by-Example Spoken Term Detection for MediaEval 2015: Post-Evaluation Analysis.},
+ journal = {Interspeech},
+ year = {2016},
+}
+@article{mediaeval00484,
+ author = {X Li and H Xianyu and J Tian and W Chen and F Meng and M Xu and L Cai},
+ title = {A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2016},
+}
+@article{mediaeval00010,
+ author = {M Skácel and M Karafiát and L Ondel and A Uchytil and I Szöke},
+ title = {BUT Zero-Cost Speech Recognition 2016 System},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016}
+}
+@article{mediaeval00485,
+ author = {S Chen and Q Jin},
+ title = {RUC at MediaEval 2016 Emotional Impact of Movies Task: Fusion of Multimodal Features.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00486,
+ author = {C Boididou and SE Middleton and S Papadopoulos and D Nguyen and D Tien and M Riegler and G Boato and A Petlund and Y Kompatsiaris},
+ title = {The VMU participation@ verifying multimedia use 2016.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00487,
+ author = {QT Phan and A Budroni and C Pasquini and FGB De Natale},
+ title = {A Hybrid Approach for Multimedia Use Verification.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00488,
+ author = {C Maigrot and V Claveau and E Kijak and R Sicre},
+ title = {Mediaeval 2016: A multimodal system for the verifying multimedia use task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00047,
+ author = {A Castellanos and X Benavent and A Garc{\'\i}a-Serrano and E de Ves},
+ title = {UNED-UV@ 2016 Retrieving Diverse Social Images Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016}
+}
+@article{mediaeval00489,
+ author = {S Rayatdoost and M Soleymani},
+ title = {Ranking Images and Videos on Visual Interestingness by Visual Sentiment Features.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00490,
+ author = {B Ionescu and AL Gînscă and B Boteanu and M Lupu and A Popescu and H M{\"u}ller},
+ title = {Div150Multi: a social image retrieval result diversification dataset with multi-topic queries.},
+ journal = {International Conference on Multimedia Systems},
+ year = {2016},
+}
+@article{mediaeval00491,
+ author = {A Jan and YFBA Gaus and H Meng and F Zhang},
+ title = {BUL in MediaEval 2016 Emotional Impact of Movies Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00492,
+ author = {Y Liu and Z Gu and Y Zhang and Y Liu},
+ title = {Mining Emotional Features of Movies.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00493,
+ author = {J Cao and Z Jin and Y Zhang and Y Zhang},
+ title = {MCG-ICT at MediaEval 2016 Verifying Tweets from both Text and Visual Content.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00494,
+ author = {H Xianyu and X Li and W Chen and F Meng and J Tian and M Xu and L Cai},
+ title = {SVR based double-scale regression for dynamic emotion prediction in music.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2016},
+}
+@article{mediaeval00495,
+ author = {G Mu and H Cao and Q Jin},
+ title = {Violent scene detection using convolutional neural networks and deep audio features},
+ journal = {Chinese Conference on Pattern Recognition},
+ year = {2016},
+}
+@article{mediaeval00496,
+ author = {MC Madhavi and HA Patil},
+ title = {Modification in sequential dynamic time warping for fast computation of query-by-example spoken term detection task},
+ journal = {International Conference on Signal Processing and Communications (SPCOM)},
+ year = {2016},
+}
+@article{mediaeval00497,
+ author = {Y Liu and Z Gu and Y Cheung},
+ title = {Supervised Manifold Learning for Media Interestingness Prediction.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00498,
+ author = {C Liem},
+ title = {TUD-MMC at MediaEval 2016: Predicting Media Interestingness Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00499,
+ author = {E Acar and F Hopfgartner and S Albayrak},
+ title = {Breaking down violence detection: Combining divide-et-impera and coarse-to-fine strategies},
+ journal = {Neurocomputing},
+ year = {2016},
+}
+@article{mediaeval00500,
+ author = {A Castellanos and X Benavent and A Garc{\'\i}a-Serrano and E de Ves and J Cigarr{\'a}n},
+ title = {UNED-UV@ retrieving diverse social images task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00501,
+ author = {K Ahmad and F De Natale and G Boato and A Rosani},
+ title = {A hierarchical approach to event discovery from single images using MIL framework.},
+ journal = {IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
+ year = {2016},
+}
+@article{mediaeval00502,
+ author = {M Zeppelzauer and D Schopfhauser},
+ title = {Multimodal classification of events in social media},
+ journal = {Image and Vision Computing},
+ year = {2016},
+}
+@article{mediaeval00503,
+ author = {B Boteanu and MG Constantin and B Ionescu},
+ title = {LAPI@ 2016 Retrieving Diverse Social Images Task: A Pseudo-Relevance Feedback Diversification Perspective.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00504,
+ author = {A Aljanaki and YH Yang and M Soleymani},
+ title = {Emotion in Music task: Lessons Learned.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00505,
+ author = {S Fukuyama and M Goto},
+ title = {Music emotion recognition with adaptive aggregation of Gaussian process regressors},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2016},
+}
+@article{mediaeval00506,
+ author = {T Anastasia and H Leontios},
+ title = {Auth-sgp in mediaeval 2016 emotional impact of movies task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00507,
+ author = {B Xu and Y Fu and YG Jiang},
+ title = {BigVid at MediaEval 2016: predicting interestingness in images and videos},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00508,
+ author = {X Li and J Tian and M Xu and Y Ning and L Cai},
+ title = {DBLSTM-based multi-scale fusion for dynamic emotion prediction in music},
+ journal = {IEEE International Conference on Multimedia and Expo (ICME)},
+ year = {2016},
+}
+@article{mediaeval00509,
+ author = {S Tollari},
+ title = {UPMC at mediaeval 2016 retrieving diverse social images task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00510,
+ author = {J Xue and K Eguchi},
+ title = {Sequential correspondence hierarchical Dirichlet processes for video data analysis},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2016},
+}
+@article{mediaeval00511,
+ author = {W Zhang and CW Ngo and X Cao},
+ title = {Hyperlink-aware object retrieval},
+ journal = {IEEE Transactions on Image Processing},
+ year = {2016},
+}
+@article{mediaeval00512,
+ author = {A Khwileh and D Ganguly and GJF Jones},
+ title = {Utilisation of metadata fields and query expansion in cross-lingual search of user-generated internet video},
+ journal = {Journal of Artificial Intelligence Research},
+ year = {2016},
+}
+@article{mediaeval00513,
+ author = {RFE Sutcliffe and T Collins and EH Hovy and R Lewis and C Fox and DL Root},
+ title = {The C@merata task at MediaEval 2016: Natural Language Queries Derived from Exam Papers, Articles and Other Sources against Classical Music Scores in MusicXML.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00514,
+ author = {G Kordopatis-Zilos and S Papadopoulos and Y Kompatsiaris},
+ title = {In-depth exploration of geotagging performance using sampling strategies on yfcc100m.},
+ journal = {ACM Workshop on Multimedia COMMONS},
+ year = {2016},
+}
+@article{mediaeval00515,
+ author = {J Proença and F Perdigão},
+ title = {Segmented Dynamic Time Warping for Spoken Query-by-Example Search.},
+ journal = {INTERSPEECH},
+ year = {2016},
+}
+@article{mediaeval00516,
+ author = {G Kordopatis-Zilos and A Popescu and S Papadopoulos and Y Kompatsiaris},
+ title = {Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00517,
+ author = {SK Singh and D Rafiei},
+ title = {Geotagging Flickr Photos And Videos Using Language Models.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00518,
+ author = {V Lam and T Do and S Phan and DD Le and Shinichi Satoh and ...},
+ title = {NII-UIT at MediaEval 2016 Predicting Media Interestingness Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
+}
+@article{mediaeval00519,
+ author = {J Almeida},
+ title = {UNIFESP at MediaEval 2016: Predicting Media Interestingness Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00319,
- author = {Z Paróczi and B Fodor and G Szücs},
- title = {Re-Ranking the Image Search Results for Relevance and Diversity in MediaEval 2014 Challenge.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{mediaeval00520,
+ author = {X Li and Y Huo and Q Jin and J Xu},
+ title = {Detecting Violence in Video using Subclasses},
+ journal = {ACM international conference on Multimedia},
+ year = {2016},
}
-@article{mediaeval00320,
- author = {V Imbrasaite and P Robinson},
- title = {Music Emotion Tracking with Continuous Conditional Neural Fields and Relative Representation.},
+@article{mediaeval00521,
+ author = {SN Tiwari and NQK Duong and F Lefebvre and CH Demarty and B Huet and L Chevallier},
+ title = {Deep features for multimodal emotion classification},
+ year = {2016},
+}
+@article{mediaeval00522,
+ author = {G Sargent and GB de Fonseca and IL Freire and R Sicre and Z Do Patroc{\'\i}nio Jr and S Guimar{\~a}es and G Gravier},
+ title = {Puc minas and irisa at multimodal person discovery.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00321,
- author = {B Vandersmissen and A Tomar and F Godin and W De Neve and R Van de Walle},
- title = {Ghent University-iMinds at MediaEval 2014 Diverse Images: Adaptive Clustering with Deep Features.},
+@article{mediaeval00523,
+ author = {M Riegler and C Spampinato and M Larson and C Griwodz and P Halvorsen},
+ title = {The MediaEval 2016 Context of Experience Task: Recommending Videos Suiting a Watching Situation.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00322,
- author = {MI Sarac and P Duygulu},
- title = {Bilkent-RETINA at retrieving diverse social images task of MediaEval 2014},
+@article{mediaeval00524,
+ author = {J Parekh and S Parekh},
+ title = {The MLPBOON Predicting Media Interestingness System for MediaEval 2016.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00323,
- author = {M Zaharieva and P Schwab},
- title = {A Unified Framework for Retrieving Diverse Social Images.},
+@article{mediaeval00525,
+ author = {V Lam and SP Le and T Do and TD Ngo and DD Le and DA Duong},
+ title = {Computational optimization for violent scenes detection.},
+ journal = {IEEE International Conference on Computer, Control, Informatics and its Applications (IC3INA)},
+ year = {2016},
+}
+@article{mediaeval00526,
+ author = {AB Vasudevan and M Gygli and A Volokitin and L Van Gool},
+ title = {ETH-CVL@ MediaEval 2016: Textual-Visual Embeddings and Video2GIF for Video Interestingness.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00324,
- author = {A Rosani and G Boato and FGB De Natale},
- title = {Eventmask: a gamebased analysis of event-related saliency in photo galleries},
- journal = {Proceedings of the IEEE ICASSP, Show and Tell},
- year = {2014},
+@article{mediaeval00527,
+ author = {CC Tan and CW Ngo},
+ title = {On the use of commonsense ontology for multimedia event recounting},
+ journal = {International journal of multimedia information retrieval},
+ year = {2016},
}
-@article{mediaeval00325,
- author = {J Chiu and A Rudnicky},
- title = {LACS System analysis on retrieval models for the MediaEval 2014 Search and Hyperlinking Task},
+@article{mediaeval00528,
+ author = {M Zaharieva},
+ title = {An Adaptive Clustering Approach for the Diversification of Image Retrieval Results.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00326,
- author = {M Morchid and R Dufour and G Linares},
- title = {A Combined Thematic and Acoustic Approach for a Music Recommendation Service in TV Commercials.},
- journal = {ISMIR},
- year = {2014},
+@article{mediaeval00529,
+ author = {N Le and S Meignier and JM Odobez},
+ title = {Eumssi team at the mediaeval person discovery challenge 2016},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
}
-@article{mediaeval00327,
- author = {S Wan},
- title = {A description of the CLAS system at C@merata 2014},
+@article{mediaeval00530,
+ author = {A Katsiavalos},
+ title = {DMUN: A Textual Interface for Content-Based Music Information Retrieval in the C@merata task for MediaEval 2016.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00328,
- author = {C Pantoja and E Izquierdo},
- title = {MediaEval 2014 Visual Privacy Task: De-identification and Re-identification of Subjects in CCTV.},
+@article{mediaeval00531,
+ author = {A Aljanaki},
+ title = {Emotion in Music: representation and computational modeling},
+ journal = {PhD dissertation},
+ year = {2016},
+}
+@article{mediaeval00532,
+ author = {J Choi and M Larson and X Li and G Friedland and A Hanjalic},
+ title = {Where to be wary: The impact of widespread photo-taking and image enhancement practices on users' geo-privacy.},
+ journal = {arXiv preprint},
+ year = {2016},
+}
+@article{mediaeval00533,
+ author = {JAV Muñoz and LT Li and ÍC Dourado and K Nogueira and SG Fadel and OAB Penatti and J Almeida and LAM Pereira and RT Calumby and JA dos Santos and others},
+ title = {RECOD@ Placing Task of MediaEval 2016: A Ranking Fusion Approach for Geographic-Location Prediction of Multimedia Objects.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00329,
- author = {D Maniry and E Acar and S Albayrak},
- title = {TUB-IRML at MediaEval 2014 Visual Privacy Task: Privacy Filtering through Blurring and Color Remapping.},
+@article{mediaeval00534,
+ author = {M Mytrova},
+ title = {The KIAM System in the C@merata Task at MediaEval 2016.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00330,
- author = {S Goto and T Aoki},
- title = {Violent Scenes Detection based on Automatically-generated Mid-level Violent Concepts},
- journal = {Computer Vision Winter Workshop},
- year = {2014},
+@article{mediaeval00535,
+ author = {M Buccoli and M Zanoni and G Fazekas and A Sarti and MB Sandler},
+ title = {A Higher-Dimensional Expansion of Affective Norms for English Terms for Music Tagging.},
+ journal = {ISMIR},
+ year = {2016},
}
-@article{mediaeval00331,
- author = {J Vavrek and P Viszlay and M Lojka and M Pleva and J Juhár},
- title = {TUKE System for MediaEval 2014 QUESST.},
+@article{mediaeval00536,
+ author = {CD Ferreira and RT Calumby and IBAC Araujo and IC Dourado and JAV Munoz and OAB Penatti and LT Li and J Almeida and R da Silva Torres},
+ title = {Recod@ MediaEval 2016: Diverse Social Images Retrieval.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00332,
- author = {M Ruocco},
- title = {Geo-Temporal Mining and Searching of Events from Web-based Image Collections.},
- journal = {PhD dissertation},
- year = {2014},
+@article{mediaeval00537,
+ author = {N Kumar and T Guha and CW Huang and C Vaz and SS Narayanan},
+ title = {Novel affective features for multiscale prediction of emotion in music.},
+ journal = {IEEE International Workshop on Multimedia Signal Processing (MMSP) },
+ year = {2016},
}
-@article{mediaeval00333,
- author = {AR Simon and C Guinaudeau and P S{\'e}billot and G Gravier},
- title = {Investigating domain-independent NLP techniques for precise target selection in video hyperlinking.},
- journal = {ISCA/IEEE Workshop on Speech, Language and Audio in Multimedia},
- year = {2014},
+@article{mediaeval00538,
+ author = {YC Wu and HH Chen},
+ title = {Generation of Affective Accompaniment in Accordance With Emotion Flow},
+ journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
+ year = {2016},
}
-@article{mediaeval00334,
- author = {B Safadi and M Sahuguet and B Huet},
- title = {Linking text and visual concepts semantically for cross modal multimedia search},
- journal = {International Conference on Image Processing (ICIP)},
- year = {2014},
+@article{mediaeval00539,
+ author = {MA Abebe and J Tekli and F Getahun and G Tekli and R Chbeir},
+ title = {A General Multimedia Representation Space Model toward Event-Based Collective Knowledge Management.},
+ journal = {EEE International Conference on Computational Science and Engineering (CSE)},
+ year = {2016},
}
-@article{mediaeval00335,
- author = {N Kumar and SS Narayanan},
- title = {Detection of Musical Event Drop from Crowdsourced Annotations Using a Noisy Channel Model.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+@article{mediaeval00540,
+ author = {N Alsaedi and P Burnap and O Rana},
+ title = {Sensing Real-World Events Using Social Media Data and a Classification-Clustering Framework},
+ journal = {IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
+ year = {2016},
}
-@article{mediaeval00336,
- author = {DÓ Maidín},
- title = {OMDN at the MediaEval 2014 C@merata Task.},
+@article{mediaeval00541,
+ author = {K Pogorelov and M Riegler and P Halvorsen and C Griwodz},
+ title = {Simula@ MediaEval 2016 Context of Experience Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00337,
- author = {L Weiland and SP Ponzetto},
- title = {DWS at MediaEval 2014 Retrieving Diverse Social Images: Approaches for Visual and Textual Runs.},
+@article{mediaeval00542,
+ author = {T Godambe and N Kumar and P Kumar and V Raghavendra and A Ganapathiraju},
+ title = {ININ Submission to Zero Cost ASR Task at MediaEval 2016.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00338,
- author = {A Terumasa and G Shinichi},
- title = {Automatic Mid-Level Concepts Clustering for Violent Movie Scenes Detection},
- journal = {Journal of Mathematics and System Science},
- year = {2014},
+@article{mediaeval00543,
+ author = {B Wang and CCS Liem},
+ title = {TUD-MMC at MediaEval 2016: Context of Experience task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
}
-@article{mediaeval00339,
- author = {N Derbas and G. Quénot},
- title = {Joint Audio-Visual Words for Violent Scenes Detection in Movies},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2014},
+@article{mediaeval00544,
+ author = {P Sgouroglou and TMA Aziz and M Lux},
+ title = {Context of Experience-MediaEval Submission of ITEC/AAU.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
}
-@article{mediaeval00340,
- author = {TL Lin},
- title = {A mutimodal framework for geocoding digital objects},
- year = {2014},
+@article{mediaeval00036,
+ author = {YFA Gaus and H Meng and A Jan and J Liu and R Qin and F Zhang},
+ title = {MediaEval 2016: Performance of Hand-crafted and Deep features with Exponent Weighted Decision Fusion for Prediction Emotional Impact of Movies},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016}
}
-@article{mediaeval00341,
- author = {W Khan},
- title = {A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time.},
- journal = {PhD dissertation},
- year = {2014},
+@article{mediaeval00545,
+ author = {L Wang and C Ni and CC Leung and C You and L Xie and H Xu and X Xiao and TL Nwe and ES Chng and B Ma and others},
+ title = {The NNI Vietnamese Speech Recognition System for MediaEval 2016.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2016},
}
-@article{mediaeval00342,
- author = {B Boteanu and I Mironica and AL Radu and B Ionescu},
- title = {LAPI@ 2014 Retrieving Diverse Social Images Task: A Relevance Feedback Diversification Perspective.},
+@article{mediaeval00546,
+ author = {F Nishi and N Inoue and K Iwano and K Shinoda},
+ title = {Tokyo Tech at MediaEval 2016 Multimodal Person Discovery in Broadcast TV task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
+}
+@article{mediaeval00547,
+ author = {CCS Liem},
+ title = {Crowdsourcing audience perspectives on classical music},
+ journal = {2016 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
+ year = {2016},
+}
+@article{mediaeval00548,
+ author = {Z Yang and Q Li and W Liu and Y Ma},
+ title = {Class-Wise Data Reconstruction for Event Detection from Flickr Streaming Data},
+ journal = {IEEE International conference on ubi-media computing},
+ year = {2016},
+}
+@article{mediaeval00549,
+ author = {A Caranica and H Cucu and A Buzo},
+ title = {Exploring an unsupervised, language independent, spoken document retrieval system},
+ journal = {International Workshop on Content-Based Multimedia Indexing (CBMI)},
+ year = {2016},
}
-@article{mediaeval00343,
- author = {AR Simon and G Gravier and P S{\'e}billot and MFM IRISA},
- title = {Hyperlinking Task},
+@article{mediaeval00550,
+ author = {T Pellegrini and V Barrière},
+ title = {Sous-continents Estimation of Emotion in Music with Recurrent Neural Networks},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00344,
- author = {H Karamti and M Tmar and F Gargouri},
- title = {MIRACL's participation at MediaEval 2014 Retrieving Diverse Social Images Task.},
+@article{mediaeval00551,
+ author = {B Xu and G Fan and D Yang},
+ title = {Topic Modeling Based Image Clustering by Events in Social Media},
+ journal = {Scientific Programming},
+ year = {2016},
+}
+@article{mediaeval00552,
+ author = {M Eskevich and GJF Jones and R Aly and R Ordelman and B Huet},
+ title = {Pursuing a Moving Target. Iterative Use of Benchmarking of a Task to Understand the Task},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016},
}
-@article{mediaeval00345,
- author = {W Bailer and H Stiegler},
- title = {JRS at Search and Hyperlinking of Television Content Task.},
+@article{mediaeval00040,
+ author = {AE Goksu Erdogan and E Erdem},
+ title = {HUCVL at MediaEval 2016: Predicting Interesting Key Frames with Deep Models},
journal = {MediaEval Working Notes Proceedings},
- year = {2014},
+ year = {2016}
}
@article{mediaeval00347,
author = {H Wang and T Lee and CC Leung and B Ma and H Li},
@@ -2136,6 +5477,14 @@ @article{mediaeval00400
journal = {MediaEval Working Notes Proceedings},
year = {2015},
}
+@incollection{larson2015benchmark,
+ title={The benchmark as a research catalyst: Charting the progress of geo-prediction for social multimedia},
+ author={Larson, Martha and Kelm, Pascal and Rae, Adam and Hauff, Claudia and Thomee, Bart and Trevisiol, Michele and Choi, Jaeyoung and Van Laere, Olivier and Schockaert, Steven and Jones, Gareth JF and others},
+ booktitle={Multimodal Location Estimation of Videos and Images},
+ pages={5--40},
+ year={2015},
+ publisher={Springer}
+}
@article{mediaeval00401,
author = {P Lopez-Otero and L Docio-Fernandez and C Garcia-Mateo},
title = {GTM-UVigo systems for the Query-by-Example search on speech task at MediaEval 2015},
@@ -2460,2186 +5809,1906 @@ @article{mediaeval00454
year = {2015},
}
@article{mediaeval00455,
- author = {CT Chung and Yang-de Chen},
- title = {NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection using Deep and Recurrent Neural Networks.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015},
-}
-@article{mediaeval00456,
- author = {M Skácel},
- title = {Query-by-Example Spoken Term Detection},
- journal = {Proceedings of Excel@ FIT},
- year = {2015},
-}
-@article{mediaeval00457,
- author = {A Caranica and H Cucu and A Buzo and C Burileanu},
- title = {Exploring Spoken Term Detection with a Robust Multi-Language Phone Recognition System},
- year = {2015},
-}
-@article{mediaeval00043,
- author = {M Gygli and L Van Gool},
- title = {ETH-CVL@ MediaEval 2015: Learning Objective Functions for Improved Image Retrieval},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015}
-}
-@article{mediaeval00458,
- author = {K Markov and T Matsui},
- title = {Dynamic Music Emotion Recognition Using Kernel Bayes' Filter.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015},
-}
-@article{mediaeval00459,
- author = {V Ekambaram and K Ramchandran and J Choi and G Friedland},
- title = {Collaborative Multimodal Location Estimation of Consumer Media.},
- journal = {Multimodal Location Estimation of Videos and Images},
- year = {2015},
-}
-@article{mediaeval00460,
- author = {H Lei and J Choi and G Friedland},
- title = {Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments},
- journal = {Multimodal Location Estimation of Videos and Images},
- year = {2015},
-}
-@article{mediaeval00461,
- author = {A Rosani},
- title = {Multimedia Content Analysis for Event Detection},
- journal = {PhD Tesis},
- year = {2015},
-}
-@article{mediaeval00462,
- author = {A Lidon and X Gir{\'o} Nieto and M Bola{\~n}os and P Radeva and M Seidl and M Zeppelzauer},
- title = {UPC-UB-STP@ MediaEval 2015 diversity task: iterative reranking of relevant images.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015},
-}
-@article{mediaeval00463,
- author = {T Agrawal and R Gupta and S Narayanan},
- title = {Retrieving Social Images using Relevance Filtering and Diverse Selection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015},
-}
-@article{mediaeval00464,
- author = {S Dudy and S Bedrick},
- title = {OHSU@ MediaEval 2015: Adapting Textual Techniques to Multimedia Search.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015},
-}
-@article{mediaeval00465,
- author = {S Schmiedeke and P Kelm and L Goldmann},
- title = {Imcube@ MediaEval 2015 Retrieving Diverse Social Images Task: Multimodal Filtering and Re-ranking.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2015},
-}
-@article{mediaeval00466,
- author = {MA Abebe and F Getahun and S Asres and R Chbeir},
- title = {Event extraction for collective knowledge in multimedia digital EcoSystem},
- journal = {AFRICON},
- year = {2015},
-}
-@article{mediaeval00467,
- author = {M Eskevich and QM Bui and HA Le and B Huet},
- title = {Exploring Video Hyperlinking in Broadcast Media},
- journal = {Workshop on Speech, Language & Audio in Multimedia},
- year = {2015},
-}
-@article{mediaeval00468,
- author = {E Dellandréa and L Chen and Y Baveye and M Sj{\"o}berg and C Chamaret},
- title = {The mediaeval 2016 emotional impact of movies task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00469,
- author = {N Panagiotou and I Katakis and D Gunopulos},
- title = {Detecting events in online social networks: Definitions, trends and challenges},
- journal = {Solving Large Scale Learning Tasks. Challenges and Algorithms},
- year = {2016},
-}
-@article{mediaeval00470,
- author = {I. Mironică and IC Duta and B Ionescu and N Sebe},
- title = {A modified vector of locally aggregated descriptors approach for fast video classification},
- journal = {Multimedia Tools and Applications},
- year = {2016},
-}
-@article{mediaeval00471,
- author = {S Papadopoulos and K Bontcheva and E Jaho and M Lupu and C Castillo},
- title = {Overview of the special issue on trust and veracity of information in social media.},
- journal = {ACM Transactions on Information Systems (TOIS)},
- year = {2016},
-}
-@article{mediaeval00472,
- author = {T Kaneko and K Yanai},
- title = {Event photo mining from twitter using keyword bursts and image clustering},
- journal = {Neurocomputing},
- year = {2016},
-}
-@article{mediaeval00473,
- author = {B Ionescu and A Popescu and AL Radu and H Muller},
- title = {Result diversification in social image retrieval: a benchmarking framework},
- journal = {Multimedia Tools and Applications},
- year = {2016},
-}
-@article{mediaeval00474,
- author = {V Vukotić and C Raymond and G Gravier},
- title = {Bidirectional joint representation learning with symmetrical deep neural networks for multimodal and crossmodal applications.},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2016},
-}
-@article{mediaeval00475,
- author = {PC Ribeiro and R Audigier and QC Pham},
- title = {RIMOC, a feature to discriminate unstructured motions: Application to violence detection for video-surveillance},
- journal = {Computer vision and image understanding},
- year = {2016},
-}
-@article{mediaeval00476,
- author = {P Lopez-Otero and L Docio-Fernandez and C Garcia-Mateo},
- title = {Finding relevant features for zero-resource query-by-example search on speech.},
- journal = {Speech Communication},
- year = {2016},
-}
-@article{mediaeval00477,
- author = {Y Shen and CH Demarty and NQK Duong},
- title = {Technicolor@ MediaEval 2016 Predicting Media Interestingness Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00478,
- author = {G Boato and DT Dang-Nguyen and O Muratov and N Alajlan and FGB De Natale},
- title = {Exploiting visual saliency for increasing diversity of image retrieval results.},
- journal = {Multimedia Tools and Applications},
- year = {2016},
-}
-@article{mediaeval00479,
- author = {I. Mironică and B Ionescu and J Uijlings and N Sebe},
- title = {Fisher kernel temporal variation-based relevance feedback for video retrieval},
- journal = {Computer vision and image understanding},
- year = {2016},
-}
-@article{mediaeval00480,
- author = {H Xu and J Hou and X Xiao and CC Leung and L Wang and H Lv and L Xie and B Ma and ES Chng and H Li and others},
- title = {Approximate search of audio queries by using DTW with phone time boundary and data augmentation.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2016},
-}
-@article{mediaeval00481,
- author = {H Bredin and C Barras and C Guinaudeau},
- title = {Multimodal person discovery in broadcast TV at MediaEval 2016},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00482,
- author = {Y Ma and Z Ye and M Xu},
- title = {THU-HCSI at MediaEval 2016: Emotional Impact of Movies Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00483,
- author = {CC Leung and L Wang and H Xu and J Hou and ...},
- title = {Toward High-Performance Language-Independent Query-by-Example Spoken Term Detection for MediaEval 2015: Post-Evaluation Analysis.},
- journal = {Interspeech},
- year = {2016},
-}
-@article{mediaeval00484,
- author = {X Li and H Xianyu and J Tian and W Chen and F Meng and M Xu and L Cai},
- title = {A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2016},
-}
-@article{mediaeval00010,
- author = {M Skácel and M Karafiát and L Ondel and A Uchytil and I Szöke},
- title = {BUT Zero-Cost Speech Recognition 2016 System},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016}
-}
-@article{mediaeval00485,
- author = {S Chen and Q Jin},
- title = {RUC at MediaEval 2016 Emotional Impact of Movies Task: Fusion of Multimodal Features.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00486,
- author = {C Boididou and SE Middleton and S Papadopoulos and D Nguyen and D Tien and M Riegler and G Boato and A Petlund and Y Kompatsiaris},
- title = {The VMU participation@ verifying multimedia use 2016.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00487,
- author = {QT Phan and A Budroni and C Pasquini and FGB De Natale},
- title = {A Hybrid Approach for Multimedia Use Verification.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00488,
- author = {C Maigrot and V Claveau and E Kijak and R Sicre},
- title = {Mediaeval 2016: A multimodal system for the verifying multimedia use task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00047,
- author = {A Castellanos and X Benavent and A Garc{\'\i}a-Serrano and E de Ves},
- title = {UNED-UV@ 2016 Retrieving Diverse Social Images Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016}
-}
-@article{mediaeval00489,
- author = {S Rayatdoost and M Soleymani},
- title = {Ranking Images and Videos on Visual Interestingness by Visual Sentiment Features.},
+ author = {CT Chung and Yang-de Chen},
+ title = {NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection using Deep and Recurrent Neural Networks.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2015},
}
-@article{mediaeval00490,
- author = {B Ionescu and AL Gînscă and B Boteanu and M Lupu and A Popescu and H M{\"u}ller},
- title = {Div150Multi: a social image retrieval result diversification dataset with multi-topic queries.},
- journal = {International Conference on Multimedia Systems},
- year = {2016},
+@article{mediaeval00456,
+ author = {M Skácel},
+ title = {Query-by-Example Spoken Term Detection},
+ journal = {Proceedings of Excel@ FIT},
+ year = {2015},
}
-@article{mediaeval00491,
- author = {A Jan and YFBA Gaus and H Meng and F Zhang},
- title = {BUL in MediaEval 2016 Emotional Impact of Movies Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+@article{mediaeval00457,
+ author = {A Caranica and H Cucu and A Buzo and C Burileanu},
+ title = {Exploring Spoken Term Detection with a Robust Multi-Language Phone Recognition System},
+ year = {2015},
}
-@article{mediaeval00492,
- author = {Y Liu and Z Gu and Y Zhang and Y Liu},
- title = {Mining Emotional Features of Movies.},
+@article{mediaeval00043,
+ author = {M Gygli and L Van Gool},
+ title = {ETH-CVL@ MediaEval 2015: Learning Objective Functions for Improved Image Retrieval},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2015}
}
-@article{mediaeval00493,
- author = {J Cao and Z Jin and Y Zhang and Y Zhang},
- title = {MCG-ICT at MediaEval 2016 Verifying Tweets from both Text and Visual Content.},
+@article{mediaeval00458,
+ author = {K Markov and T Matsui},
+ title = {Dynamic Music Emotion Recognition Using Kernel Bayes' Filter.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2015},
}
-@article{mediaeval00494,
- author = {H Xianyu and X Li and W Chen and F Meng and J Tian and M Xu and L Cai},
- title = {SVR based double-scale regression for dynamic emotion prediction in music.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2016},
+@article{mediaeval00459,
+ author = {V Ekambaram and K Ramchandran and J Choi and G Friedland},
+ title = {Collaborative Multimodal Location Estimation of Consumer Media.},
+ journal = {Multimodal Location Estimation of Videos and Images},
+ year = {2015},
}
-@article{mediaeval00495,
- author = {G Mu and H Cao and Q Jin},
- title = {Violent scene detection using convolutional neural networks and deep audio features},
- journal = {Chinese Conference on Pattern Recognition},
- year = {2016},
+@article{mediaeval00460,
+ author = {H Lei and J Choi and G Friedland},
+ title = {Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments},
+ journal = {Multimodal Location Estimation of Videos and Images},
+ year = {2015},
}
-@article{mediaeval00496,
- author = {MC Madhavi and HA Patil},
- title = {Modification in sequential dynamic time warping for fast computation of query-by-example spoken term detection task},
- journal = {International Conference on Signal Processing and Communications (SPCOM)},
- year = {2016},
+@article{mediaeval00461,
+ author = {A Rosani},
+ title = {Multimedia Content Analysis for Event Detection},
+ journal = {PhD Tesis},
+ year = {2015},
}
-@article{mediaeval00497,
- author = {Y Liu and Z Gu and Y Cheung},
- title = {Supervised Manifold Learning for Media Interestingness Prediction.},
+@article{mediaeval00462,
+ author = {A Lidon and X Gir{\'o} Nieto and M Bola{\~n}os and P Radeva and M Seidl and M Zeppelzauer},
+ title = {UPC-UB-STP@ MediaEval 2015 diversity task: iterative reranking of relevant images.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2015},
}
-@article{mediaeval00498,
- author = {C Liem},
- title = {TUD-MMC at MediaEval 2016: Predicting Media Interestingness Task.},
+@article{mediaeval00463,
+ author = {T Agrawal and R Gupta and S Narayanan},
+ title = {Retrieving Social Images using Relevance Filtering and Diverse Selection.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2015},
}
-@article{mediaeval00499,
- author = {E Acar and F Hopfgartner and S Albayrak},
- title = {Breaking down violence detection: Combining divide-et-impera and coarse-to-fine strategies},
- journal = {Neurocomputing},
- year = {2016},
+@article{mediaeval00464,
+ author = {S Dudy and S Bedrick},
+ title = {OHSU@ MediaEval 2015: Adapting Textual Techniques to Multimedia Search.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2015},
}
-@article{mediaeval00500,
- author = {A Castellanos and X Benavent and A Garc{\'\i}a-Serrano and E de Ves and J Cigarr{\'a}n},
- title = {UNED-UV@ retrieving diverse social images task.},
+@article{mediaeval00465,
+ author = {S Schmiedeke and P Kelm and L Goldmann},
+ title = {Imcube@ MediaEval 2015 Retrieving Diverse Social Images Task: Multimodal Filtering and Re-ranking.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2015},
}
-@article{mediaeval00501,
- author = {K Ahmad and F De Natale and G Boato and A Rosani},
- title = {A hierarchical approach to event discovery from single images using MIL framework.},
- journal = {IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
- year = {2016},
+@article{mediaeval00466,
+ author = {MA Abebe and F Getahun and S Asres and R Chbeir},
+ title = {Event extraction for collective knowledge in multimedia digital EcoSystem},
+ journal = {AFRICON},
+ year = {2015},
}
-@article{mediaeval00502,
- author = {M Zeppelzauer and D Schopfhauser},
- title = {Multimodal classification of events in social media},
- journal = {Image and Vision Computing},
- year = {2016},
+@article{mediaeval00467,
+ author = {M Eskevich and QM Bui and HA Le and B Huet},
+ title = {Exploring Video Hyperlinking in Broadcast Media},
+ journal = {Workshop on Speech, Language & Audio in Multimedia},
+ year = {2015},
}
-@article{mediaeval00503,
- author = {B Boteanu and MG Constantin and B Ionescu},
- title = {LAPI@ 2016 Retrieving Diverse Social Images Task: A Pseudo-Relevance Feedback Diversification Perspective.},
+@article{mediaeval00238,
+ author = {M Eskevich and R Aly and D Racca and R Ordelman and S Chen and GJF Jones},
+ title = {The search and hyperlinking task at MediaEval 2014.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00504,
- author = {A Aljanaki and YH Yang and M Soleymani},
- title = {Emotion in Music task: Lessons Learned.},
+@article{mediaeval00239,
+ author = {B Ionescu and AL Gansca and B Boteanu and A Popescu and M Lupu and H Müller},
+ title = {Retrieving Diverse Social Images at MediaEval 2014: Challenge, Dataset and Evaluation.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00505,
- author = {S Fukuyama and M Goto},
- title = {Music emotion recognition with adaptive aggregation of Gaussian process regressors},
+@article{mediaeval00240,
+ author = {LJ Rodriguez-Fuentes and A Varona and M Penagarikano and G Bordel and M Diez},
+ title = {High-performance query-by-example spoken term detection on the SWS 2013 evaluation.},
journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00506,
- author = {T Anastasia and H Leontios},
- title = {Auth-sgp in mediaeval 2016 emotional impact of movies task},
+@article{mediaeval00241,
+ author = {X Anguera and LJ Rodriguez-Fuentes and I Söke and A Buzo and F Metze},
+ title = {Query by Example Search on Speech at Mediaeval 2014.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00507,
- author = {B Xu and Y Fu and YG Jiang},
- title = {BigVid at MediaEval 2016: predicting interestingness in images and videos},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+@article{mediaeval00242,
+ author = {F Metze and X Anguera and E Barnard and M Davel and G Gravier},
+ title = {Language independent search in MediaEval's Spoken Web Search task.},
+ journal = {Computer Speech and Language},
+ year = {2014},
}
-@article{mediaeval00508,
- author = {X Li and J Tian and M Xu and Y Ning and L Cai},
- title = {DBLSTM-based multi-scale fusion for dynamic emotion prediction in music},
- journal = {IEEE International Conference on Multimedia and Expo (ICME)},
- year = {2016},
+@article{mediaeval00243,
+ author = {B Safadi and M Sahuguet and B Huet},
+ title = {When textual and visual information join forces for multimedia retrieval},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2014},
}
-@article{mediaeval00509,
- author = {S Tollari},
- title = {UPMC at mediaeval 2016 retrieving diverse social images task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+@article{mediaeval00244,
+ author = {I Söke and L B{\"u}rget and F Gr{\'e}zl and JH {\v{C}}ernocky and L Ondel},
+ title = {Calibration and fusion of query-by-example systems - BUT SWS 2013.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2014},
}
-@article{mediaeval00510,
- author = {J Xue and K Eguchi},
- title = {Sequential correspondence hierarchical Dirichlet processes for video data analysis},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2016},
+@article{mediaeval00245,
+ author = {G Petkos and S Papadopoulos and V Mezaris and Y Kompatsiaris},
+ title = {Social Event Detection at MediaEval 2014: Challenges, Datasets, and Evaluation.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00511,
- author = {W Zhang and CW Ngo and X Cao},
- title = {Hyperlink-aware object retrieval},
- journal = {IEEE Transactions on Image Processing},
- year = {2016},
+@article{mediaeval00246,
+ author = {X Anguera and LJ Rodriguez-Fuentes and I Söke and A Buzo and F Metze and M Penagarikano},
+ title = {Query-by-example spoken term detection evaluation on low-resource languages.},
+ journal = {Spoken Language Technologies for Under-Resourced Languages},
+ year = {2014},
}
-@article{mediaeval00512,
- author = {A Khwileh and D Ganguly and GJF Jones},
- title = {Utilisation of metadata fields and query expansion in cross-lingual search of user-generated internet video},
- journal = {Journal of Artificial Intelligence Research},
- year = {2016},
+@article{mediaeval00247,
+ author = {I Söke and M Skácel and L Burget},
+ title = {BUT QUESST 2014 system description.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00513,
- author = {RFE Sutcliffe and T Collins and EH Hovy and R Lewis and C Fox and DL Root},
- title = {The C@merata task at MediaEval 2016: Natural Language Queries Derived from Exam Papers, Articles and Other Sources against Classical Music Scores in MusicXML.},
+@article{mediaeval00248,
+ author = {DT Dang-Nguyen and L Piras and G Giacinto and G Boato and FGB De Natale},
+ title = {Retrieval of Diverse Images by Pre-filtering and Hierarchical Clustering.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00514,
- author = {G Kordopatis-Zilos and S Papadopoulos and Y Kompatsiaris},
- title = {In-depth exploration of geotagging performance using sampling strategies on yfcc100m.},
- journal = {ACM Workshop on Multimedia COMMONS},
- year = {2016},
+@article{mediaeval00249,
+ author = {G Petkos and S Papadopoulos and E Schinas and Y Kompatsiaris},
+ title = {Graph-based multimodal clustering for social event detection in large collections of images.},
+ journal = {International Conference on Multimedia Modeling},
+ year = {2014},
}
-@article{mediaeval00515,
- author = {J Proença and F Perdigão},
- title = {Segmented Dynamic Time Warping for Spoken Query-by-Example Search.},
- journal = {INTERSPEECH},
- year = {2016},
+@article{mediaeval00250,
+ author = {E Apostolidis and V Mezaris and M Sahuguet and B Huet and B {\v{C}}ervenkov{\'a} and D Stein and S Eickeler and JL Redondo Garcia and R Troncy and L Pikora},
+ title = {Automatic fine-grained hyperlinking of videos within a closed collection using scene segmentation.},
+ journal = {ACM international conference on Multimedia},
+ year = {2014},
}
-@article{mediaeval00516,
- author = {G Kordopatis-Zilos and A Popescu and S Papadopoulos and Y Kompatsiaris},
- title = {Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features.},
+@article{mediaeval00251,
+ author = {E Coutinho and F Weninger and BW Schuller and KR Scherer},
+ title = {The Munich LSTM-RNN Approach to the MediaEval 2014 Emotion in Music Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
+}
+@article{mediaeval00252,
+ author = {P Yang and H Xu and X Xiao and L Xie and CC Leung and H Chen and J Yu and H Lv and L Wang and SJ Leow and others},
+ title = {The NNI Query-by-Example System for MediaEval 2014.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00517,
- author = {SK Singh and D Rafiei},
- title = {Geotagging Flickr Photos And Videos Using Language Models.},
+@article{mediaeval00253,
+ author = {V Lam and DD Le and S Phan and Shinichi Satoh and DA Duong},
+ title = {NII-UIT at MediaEval 2014 Violent Scenes Detection Affect Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00518,
- author = {V Lam and T Do and S Phan and DD Le and Shinichi Satoh and ...},
- title = {NII-UIT at MediaEval 2016 Predicting Media Interestingness Task.},
+@article{mediaeval00254,
+ author = {Q Dai and Z Wu and YG Jiang and X Xue and J Tang},
+ title = {Fudan-NJUST at MediaEval 2014: Violent Scenes Detection Using Deep Neural Networks.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00519,
- author = {J Almeida},
- title = {UNIFESP at MediaEval 2016: Predicting Media Interestingness Task.},
+@article{mediaeval00255,
+ author = {G Mantena and K Prahallad},
+ title = {Use of articulatory bottle-neck features for query-by-example spoken term detection in low resource scenarios},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2014},
+}
+@article{mediaeval00256,
+ author = {N Kumar and R Gupta and T Guha and C Vaz and M Van Segbroeck and J Kim and SS Narayanan},
+ title = {Affective Feature Design and Predicting Continuous Affective Dimensions from Music.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00520,
- author = {X Li and Y Huo and Q Jin and J Xu},
- title = {Detecting Violence in Video using Subclasses},
- journal = {ACM international conference on Multimedia},
- year = {2016},
+@article{mediaeval00257,
+ author = {BE Ionescu and K Seyerlehner and I. Mironică and C Vertan and P Lambert},
+ title = {An audio-visual approach to web video categorization.},
+ journal = {Multimedia Tools and Applications},
+ year = {2014},
}
-@article{mediaeval00521,
- author = {SN Tiwari and NQK Duong and F Lefebvre and CH Demarty and B Huet and L Chevallier},
- title = {Deep features for multimodal emotion classification},
- year = {2016},
+@article{mediaeval00258,
+ author = {U Ahsan and I Essa},
+ title = {Clustering social event images using kernel canonical correlation analysis},
+ journal = {IEEE Conference on Computer Vision and Pattern Recognition Workshops},
+ year = {2014},
}
-@article{mediaeval00522,
- author = {G Sargent and GB de Fonseca and IL Freire and R Sicre and Z Do Patroc{\'\i}nio Jr and S Guimar{\~a}es and G Gravier},
- title = {Puc minas and irisa at multimodal person discovery.},
+@article{mediaeval00259,
+ author = {S Avila and D Moreira and M Perez and D Moraes and I Cota and V Testoni and E Valle and S Goldenstein and A Rocha and others},
+ title = {RECOD at MediaEval 2014: Violent scenes detection task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00523,
- author = {M Riegler and C Spampinato and M Larson and C Griwodz and P Halvorsen},
- title = {The MediaEval 2016 Context of Experience Task: Recommending Videos Suiting a Watching Situation.},
+@article{mediaeval00260,
+ author = {LT Li and DCG Pedronette and J Almeida and OAB Penatti and RT Calumby and RdaS Torres},
+ title = {A rank aggregation framework for video multimodal geocoding.},
+ journal = {Multimedia Tools and Applications},
+ year = {2014},
+}
+@article{mediaeval00261,
+ author = {K Apostolidis and C Papagiannopoulou and V Mezaris},
+ title = {CERTH at MediaEval 2014 Synchronization of Multi-User Event Media Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00524,
- author = {J Parekh and S Parekh},
- title = {The MLPBOON Predicting Media Interestingness System for MediaEval 2016.},
+@article{mediaeval00262,
+ author = {B Zhang and Y Yi and H Wang and J Yu},
+ title = {MIC-TJU at MediaEval Violent Scenes Detection (VSD) 2014.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00525,
- author = {V Lam and SP Le and T Do and TD Ngo and DD Le and DA Duong},
- title = {Computational optimization for violent scenes detection.},
- journal = {IEEE International Conference on Computer, Control, Informatics and its Applications (IC3INA)},
- year = {2016},
+@inproceedings{loni2014fashion,
+ title={Fashion 10000: an enriched social image dataset for fashion and clothing},
+ author={Loni, Babak and Cheung, Lei Yen and Riegler, Michael and Bozzon, Alessandro and Gottlieb, Luke and Larson, Martha},
+ booktitle={Proceedings of the 5th acm multimedia systems conference},
+ pages={41--46},
+ year={2014}
}
-@article{mediaeval00526,
- author = {AB Vasudevan and M Gygli and A Volokitin and L Van Gool},
- title = {ETH-CVL@ MediaEval 2016: Textual-Visual Embeddings and Video2GIF for Video Interestingness.},
+@article{mediaeval00263,
+ author = {C Bhatt and N Pappas and M Habibi and A Popescu-Belis},
+ title = {Multimodal reranking of content-based recommendations for hyperlinking video snippets.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2014},
+}
+@article{mediaeval00264,
+ author = {B do Nascimento Teixeira},
+ title = {Mtm at mediaeval 2014 violence detection task},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00527,
- author = {CC Tan and CW Ngo},
- title = {On the use of commonsense ontology for multimedia event recounting},
- journal = {International journal of multimedia information retrieval},
- year = {2016},
+@article{mediaeval00265,
+ author = {B Boteanu and I. Mironică and B Ionescu},
+ title = {A relevance feedback perspective to image search result diversification},
+ journal = {International Conference on Intelligent Computer Communication and Processing (ICCP)},
+ year = {2014},
}
-@article{mediaeval00528,
- author = {M Zaharieva},
- title = {An Adaptive Clustering Approach for the Diversification of Image Retrieval Results.},
+@article{mediaeval00266,
+ author = {M Sjöberg and I Mironica and M Schedl and B Ionescu},
+ title = {FAR at MediaEval 2014 Violent Scenes Detection: A Concept-based Fusion Approach.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00529,
- author = {N Le and S Meignier and JM Odobez},
- title = {Eumssi team at the mediaeval person discovery challenge 2016},
+@article{mediaeval00267,
+ author = {D Castán and M Rodríguez and A Ortega and C Orrite and E Lleida},
+ title = {ViVoLab and CVLab-MediaEval 2014: Violent Scenes Detection Affect Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00530,
- author = {A Katsiavalos},
- title = {DMUN: A Textual Interface for Content-Based Music Information Retrieval in the C@merata task for MediaEval 2016.},
+@article{mediaeval00268,
+ author = {N Derbas and G Quénot},
+ title = {Joint audio-visual words for violent scenes detection in movies.},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2014},
+}
+@article{mediaeval00269,
+ author = {J Proença and A Veiga and F Perdigao},
+ title = {The SPL-IT Query by Example Search on Speech system for MediaEval 2014.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00531,
- author = {A Aljanaki},
- title = {Emotion in Music: representation and computational modeling},
- journal = {PhD dissertation},
- year = {2016},
+@article{mediaeval00270,
+ author = {RFE Sutcliffe and T Crawford and C Fox and DL Root and E Hovy},
+ title = {Shared evaluation of natural language queries against classical music scores: A full description of the C@merata 2014 task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00532,
- author = {J Choi and M Larson and X Li and G Friedland and A Hanjalic},
- title = {Where to be wary: The impact of widespread photo-taking and image enhancement practices on users' geo-privacy.},
- journal = {arXiv preprint},
- year = {2016},
+@article{mediaeval00271,
+ author = {P Galuščáková and P Pecina},
+ title = {Experiments with segmentation strategies for passage retrieval in audio-visual documents},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2014},
}
-@article{mediaeval00533,
- author = {JAV Muñoz and LT Li and ÍC Dourado and K Nogueira and SG Fadel and OAB Penatti and J Almeida and LAM Pereira and RT Calumby and JA dos Santos and others},
- title = {RECOD@ Placing Task of MediaEval 2016: A Ranking Fusion Approach for Geographic-Location Prediction of Multimedia Objects.},
+@article{mediaeval00272,
+ author = {T Sutanto and R Nayak},
+ title = {Ranking Based Clustering for Social Event Detection.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00534,
- author = {M Mytrova},
- title = {The KIAM System in the C@merata Task at MediaEval 2016.},
+@article{mediaeval00273,
+ author = {LJ Rodríguez-Fuentes and A Varona and M Penagarikano and G Bordel and M Diez},
+ title = {GTTS-EHU Systems for QUESST at MediaEval 2014.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
-}
-@article{mediaeval00535,
- author = {M Buccoli and M Zanoni and G Fazekas and A Sarti and MB Sandler},
- title = {A Higher-Dimensional Expansion of Affective Norms for English Terms for Music Tagging.},
- journal = {ISMIR},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00536,
- author = {CD Ferreira and RT Calumby and IBAC Araujo and IC Dourado and JAV Munoz and OAB Penatti and LT Li and J Almeida and R da Silva Torres},
- title = {Recod@ MediaEval 2016: Diverse Social Images Retrieval.},
+@article{mediaeval00274,
+ author = {S Chen and GJF Jones and NE O'Connor},
+ title = {Dcu linking runs at mediaeval 2014: search and hyperlinking task},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00537,
- author = {N Kumar and T Guha and CW Huang and C Vaz and SS Narayanan},
- title = {Novel affective features for multiscale prediction of emotion in music.},
- journal = {IEEE International Workshop on Multimedia Signal Processing (MMSP) },
- year = {2016},
+@article{mediaeval00275,
+ author = {B Loni and J Hare and M Georgescu and M Riegler and X Zhu and M Morchid and R Dufour and M Larson},
+ title = {Getting by with a little help from the crowd: Practical approaches to social image labeling.},
+ journal = {ACM Workshop on Crowdsourcing for Multimedia},
+ year = {2014},
}
-@article{mediaeval00538,
- author = {YC Wu and HH Chen},
- title = {Generation of Affective Accompaniment in Accordance With Emotion Flow},
- journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
- year = {2016},
+@article{mediaeval00276,
+ author = {M Zaharieva and D Schopfhauser and M Del Fabro and M Zeppelzauer},
+ title = {Clustering and Retrieval of Social Events in Flickr.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00539,
- author = {MA Abebe and J Tekli and F Getahun and G Tekli and R Chbeir},
- title = {A General Multimedia Representation Space Model toward Event-Based Collective Knowledge Management.},
- journal = {EEE International Conference on Computational Science and Engineering (CSE)},
- year = {2016},
+@article{mediaeval00277,
+ author = {E Spyromitros-Xioufis and S Papadopoulos and Y Kompatsiaris and I Vlahavas},
+ title = {Socialsensor: Finding diverse images at mediaeval 2014.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00540,
- author = {N Alsaedi and P Burnap and O Rana},
- title = {Sensing Real-World Events Using Social Media Data and a Classification-Clustering Framework},
- journal = {IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
- year = {2016},
+@article{mediaeval00278,
+ author = {B Ionescu and A Popescu and H Muller and M Menendez and AL Radu},
+ title = {Benchmarking result diversification in social image retrieval.},
+ journal = {IEEE International Conference on Image Processing (ICIP)},
+ year = {2014},
}
-@article{mediaeval00541,
- author = {K Pogorelov and M Riegler and P Halvorsen and C Griwodz},
- title = {Simula@ MediaEval 2016 Context of Experience Task.},
+@article{mediaeval00279,
+ author = {S Goto and T Aoki},
+ title = {Violent scenes detection using mid-level violence clustering},
+ journal = {Computer Science CSCP},
+ year = {2014},
+}
+@article{mediaeval00280,
+ author = {Y Fan and M Xu},
+ title = {MediaEval 2014: THU-HCSIL Approach to Emotion in Music Task using Multi-level Regression.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00542,
- author = {T Godambe and N Kumar and P Kumar and V Raghavendra and A Ganapathiraju},
- title = {ININ Submission to Zero Cost ASR Task at MediaEval 2016.},
+@article{mediaeval00044,
+ author = {AL Ginsca and M Lupu and A Popescu},
+ title = {User Context Mining Module},
+ journal = {MUCKE Project Deliverable},
+ year = {2014}
+}
+@article{mediaeval00050,
+ author = {A Pournaras and V Mezaris and D Stein and S Eickeler and ...},
+ title = {LinkedTV at MediaEval 2014 Search and Hyperlinking Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014}
}
-@article{mediaeval00543,
- author = {B Wang and CCS Liem},
- title = {TUD-MMC at MediaEval 2016: Context of Experience task.},
+@article{mediaeval00053,
+ author = {P Galuščáková and P Pecina},
+ title = {CUNI at MediaEval 2014 search and hyperlinking task: Search task experiments},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00544,
- author = {P Sgouroglou and TMA Aziz and M Lux},
- title = {Context of Experience-MediaEval Submission of ITEC/AAU.},
+@article{mediaeval00281,
+ author = {E Acar and S Albayrak},
+ title = {TUB-IRML at MediaEval 2014 Violent Scenes Detection Task: Violence Modeling through Feature Space Partitioning.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00036,
- author = {YFA Gaus and H Meng and A Jan and J Liu and R Qin and F Zhang},
- title = {MediaEval 2016: Performance of Hand-crafted and Deep features with Exponent Weighted Decision Fusion for Prediction Emotional Impact of Movies},
+@article{mediaeval00282,
+ author = {AL Gînsca and A Popescu and N Rekabsaz},
+ title = {CEA LIST's Participation at the MediaEval 2014 Retrieving Diverse Social Images Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016}
+ year = {2014},
}
-@article{mediaeval00545,
- author = {L Wang and C Ni and CC Leung and C You and L Xie and H Xu and X Xiao and TL Nwe and ES Chng and B Ma and others},
- title = {The NNI Vietnamese Speech Recognition System for MediaEval 2016.},
+@article{mediaeval00283,
+ author = {A Buzo and H Cucu and C Burileanu},
+ title = {SpeeD@ MediaEval 2014: Spoken Term Detection with Robust Multilingual Phone Recognition.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00546,
- author = {F Nishi and N Inoue and K Iwano and K Shinoda},
- title = {Tokyo Tech at MediaEval 2016 Multimodal Person Discovery in Broadcast TV task.},
+@article{mediaeval00284,
+ author = {D Racca and GJF Jones},
+ title = {Dcu search runs at MediaEval 2014 search and hyperlinking},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00547,
- author = {CCS Liem},
- title = {Crowdsourcing audience perspectives on classical music},
- journal = {2016 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
- year = {2016},
+@article{mediaeval00285,
+ author = {C Gracia and X Anguera and X Binefa},
+ title = {Combining temporal and spectral information for Query-by-Example Spoken Term Detection},
+ journal = {IEEE European Signal Processing Conference (EUSIPCO)},
+ year = {2014},
}
-@article{mediaeval00548,
- author = {Z Yang and Q Li and W Liu and Y Ma},
- title = {Class-Wise Data Reconstruction for Event Detection from Flickr Streaming Data},
- journal = {IEEE International conference on ubi-media computing},
- year = {2016},
+@article{mediaeval00286,
+ author = {X Anguera and LJ Rodriguez-Fuentes and I Szőke and A Buzo and F Metze and M Penagarikano},
+ title = {Query-by-example spoken term detection on multilingual unconstrained speech.},
+ journal = {Annual Conference of the International Speech Communication Association},
+ year = {2014},
}
-@article{mediaeval00549,
- author = {A Caranica and H Cucu and A Buzo},
- title = {Exploring an unsupervised, language independent, spoken document retrieval system},
- journal = {International Workshop on Content-Based Multimedia Indexing (CBMI)},
- year = {2016},
+@article{mediaeval00287,
+ author = {H Fradi and Y Yan and JL Dugelay},
+ title = {Privacy Protection Filter Using Shape and Color Cues.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00550,
- author = {T Pellegrini and V Barrière},
- title = {Sous-continents Estimation of Emotion in Music with Recurrent Neural Networks},
+@article{mediaeval00288,
+ author = {M Riga and G Petkos and S Papadopoulos and M Schinas and ...},
+ title = {CERTH@ MediaEval 2014 Social Event Detection Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00551,
- author = {B Xu and G Fan and D Yang},
- title = {Topic Modeling Based Image Clustering by Events in Social Media},
- journal = {Scientific Programming},
- year = {2016},
+@article{mediaeval00289,
+ author = {RT Calumby and VP Santana and FS Cordeiro and OAB Penatti and LT Li and G Chiachia and RdaS Torres and others},
+ title = {Recod@ MediaEval 2014: diverse social images retrieval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00552,
- author = {M Eskevich and GJF Jones and R Aly and R Ordelman and B Huet},
- title = {Pursuing a Moving Target. Iterative Use of Benchmarking of a Task to Understand the Task},
+@article{mediaeval00290,
+ author = {W Bailer and W Weiss and C Schober and G Thallinger},
+ title = {Browsing linked video collections for media production},
+ journal = {International Conference on Multimedia Modeling},
+ year = {2014},
+}
+@article{mediaeval00291,
+ author = {P Galuščáková and M Kruliš and J Lokoč and P Pecina},
+ title = {Cuni at mediaeval 2014 search and hyperlinking task: visual and prosodic features in hyperlinking.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016},
+ year = {2014},
}
-@article{mediaeval00040,
- author = {AE Goksu Erdogan and E Erdem},
- title = {HUCVL at MediaEval 2016: Predicting Interesting Key Frames with Deep Models},
+@article{mediaeval00292,
+ author = {T Collins},
+ title = {Stravinsqi/De Montfort University at the MediaEval 2014 C@merata Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2016}
+ year = {2014},
}
-@article{mediaeval00553,
- author = {B Bischke and P Bhardwaj and A Gautam and P Helber and D Borth and A Dengel},
- title = {Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks.},
+@article{mediaeval00293,
+ author = {Á Erdélyi and T Winkler and B Rinner},
+ title = {Multi-Level Cartooning for Context-Aware Privacy Protection in Visual Sensor Networks.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00554,
- author = {Z Jin and J Cao and H Guo and Y Zhang and J Luo},
- title = {Multimodal fusion with recurrent neural networks for rumor detection on microblogs},
- journal = {ACM International conference on Multimedia},
- year = {2017},
+@article{mediaeval00294,
+ author = {E Sansone and G Boato and MS Dao},
+ title = {Synchronizing Multi-User Photo Galleries with MRF.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00555,
- author = {A Aljanaki and YH Yang and M Soleymani},
- title = {Developing a benchmark for emotional analysis of music},
- journal = {PloS one},
- year = {2017},
+@article{mediaeval00295,
+ author = {S Kesiraju and GV Mantena and K Prahallad},
+ title = {IIIT-H System for MediaEval 2014 QUESST.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00556,
- author = {Y Baveye and C Chamaret and E Dellandréa and L Chen},
- title = {Affective video content analysis: A multidisciplinary insight},
- journal = {IEEE Transactions on Affective Computing},
- year = {2017},
+@article{mediaeval00296,
+ author = {M Calvo and M Gim{\'e}nez and LF Hurtado and ES Arnal and JA G{\'o}mez},
+ title = {ELiRF at MediaEval 2014: Query by Example Search on Speech Task (QUESST).},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00557,
- author = {C Boididou and S Papadopoulos and L Apostolidis and Y Kompatsiaris},
- title = {Learning to detect misleading content on twitter.},
+@article{mediaeval00297,
+ author = {W Bailer and M Lokaj and H Stiegler},
+ title = {Context in Video Search: Is Close-by Good Enough When Using Linking?},
journal = {ACM International Conference on Multimedia Retrieval},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00558,
- author = {K Avgerinakis and A Moumtzidou and S Andreadis and E Michail and I Gialampoukidis and S Vrochidis and I Kompatsiaris},
- title = {Visual and Textual Analysis of Social Media and Satellite Images for Flood Detection@ Multimedia Satellite Task MediaEval 2017.},
+@article{mediaeval00298,
+ author = {AR Simon and G Gravier and P S{\'e}billot and MF Moens},
+ title = {IRISA and KUL at MediaEval 2014: Search and hyperlinking task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00051,
- author = {C Raymond and G Gravier},
- title = {Deep Neural Architectures for Automatic Representation Learning from Multimedia Multimodal Data},
- journal = {PhD Thesis},
- year = {2017}
+@article{mediaeval00299,
+ author = {M Zaharieva and M Riegler and M Del Fabro},
+ title = {Multimodal Synchronization of Image Galleries.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00559,
- author = {Z Yang and Q Li and W Liu and Y Ma and M Cheng},
- title = {Dual graph regularized NMF model for social event detection from Flickr data},
- journal = {ACM International Conference on World Wide Web},
- year = {2017},
+@article{mediaeval00300,
+ author = {S Denman and D Dean and C Fookes and S Sridharan},
+ title = {SAIVT-ADMRG@ mediaeval 2014 social event detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00560,
- author = {K Ahmad and K Pogorelov and M Riegler and N Conci and P Halvorsen},
- title = {CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection.},
+@article{mediaeval00301,
+ author = {C Spampinato and S Palazzo},
+ title = {PeRCeiVe Lab@ UNICT at MediaEval 2014 Diverse Images: Random Forests for Diversity-based Clustering.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00561,
- author = {X Hu and JH Lee and D Bainbridge and K Choi and P Organisciak and JS Downie},
- title = {The MIREX grand challenge: A framework of holistic user-experience evaluation in music information retrieval.},
- journal = {Journal of the Association for Information Science and Technology},
- year = {2017},
+@article{mediaeval00302,
+ author = {K Markov and T Matsui},
+ title = {Dynamic Music Emotion Recognition Using State-Space Models.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00562,
- author = {DT Dang-Nguyen and L Piras and G Giacinto and G Boato and FGB De Natale},
- title = {Multimodal retrieval with diversification and relevance feedback for tourist attraction images.},
- journal = {ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
- year = {2017},
+@article{mediaeval00303,
+ author = {JRM Palotti and N Rekabsaz and M Lupu and A Hanbury},
+ title = {TUW@ Retrieving Diverse Social Images Task 2014.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00563,
- author = {A Lidon and M Bola{\~n}os and M Dimiccoli and P Radeva and M Garolera and X Giro-i-Nieto},
- title = {Semantic summarization of egocentric photo stream events.},
- journal = {Workshop on Lifelogging Tools and Applications},
- year = {2017},
+@article{mediaeval00304,
+ author = {D Manchon-Vizuete and I Gris-Sarabia and XG Nieto},
+ title = {Photo clustering of social events by extending PhotoTOC to a rich context.},
+ journal = {ICMR Workshop on Social Events in Web Multimedia},
+ year = {2014},
}
-@article{mediaeval00564,
- author = {G Kordopatis-Zilos and S Papadopoulos and I Kompatsiaris},
- title = {Geotagging text content with language models and feature mining.},
- journal = {Proceedings of the IEEE},
- year = {2017},
+@article{mediaeval00305,
+ author = {MS Dao and AD Duong and FG De Natale},
+ title = {Unsupervised social media events clustering using user-centric parallel split-n-merge algorithms.},
+ journal = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2014},
}
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- author = {M Malik and S Adavanne and K Drossos and T Virtanen and D Ticha and R Jarina},
- title = {Stacked convolutional and recurrent neural networks for music emotion recognition.},
- journal = {arXiv preprint},
- year = {2017},
+@article{mediaeval00306,
+ author = {S Wan},
+ title = {The CLAS System at the MediaEval 2014 C@merata Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00566,
- author = {U Ahsan and C Sun and J Hays and I Essa},
- title = {Complex event recognition from images with few training examples},
- journal = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = {2017},
+@article{mediaeval00307,
+ author = {A Aljanaki and M Soleymani and F Wiering and R Veltkamp},
+ title = {Mediaeval 2014: A multimodal approach to drop detection in electronic dance music.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
+}
+@article{mediaeval00308,
+ author = {P Nowak and M Thaler and H Stiegler and W Bailer},
+ title = {JRS at Event Synchronization Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00567,
- author = {Y Liu and Z Gu and Y Cheung and KA Hua},
- title = {Multi-view manifold learning for media interestingness prediction},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2017},
+@article{mediaeval00309,
+ author = {H Wang and T Lee},
+ title = {CUHK System for QUESST Task of MediaEval 2014.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00568,
- author = {Z Yang and Q Li and Z Lu and Y Ma and Z Gong and W Liu},
- title = {Dual structure constrained multimodal feature coding for social event detection from Flickr data},
- journal = {ACM Transactions on Internet Technology (TOIT)},
- year = {2017},
+@article{mediaeval00310,
+ author = {W Yang and K Cai and B Wu and Y Wang and X Chen and D Yang and A Horner},
+ title = {Beatsens' Solution for MediaEval 2014 Emotion in Music Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00569,
- author = {MC Madhavi and HA Patil},
- title = {Partial matching and search space reduction for QbE-STD},
- journal = {Computer Speech and Language},
- year = {2017},
+@article{mediaeval00311,
+ author = {Z Paróczi and B Fodor and G Szücs},
+ title = {DCLab at MediaEval2014 Search and Hyperlinking Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00570,
- author = {D Won and ZC Steinert-Threlkeld and J Joo},
- title = {Protest activity detection and perceived violence estimation from social media images},
- journal = {ACM international conference on Multimedia},
- year = {2017},
+@article{mediaeval00312,
+ author = {AR Simon and G Gravier and P Sébillot},
+ title = {IRISA at MediaEval 2015: Search and Anchoring in Video Archives Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00571,
- author = {Z Jin and Y Yao and Y Ma and M Xu},
- title = {THUHCSI in MediaEval 2017 Emotional Impact of Movies Task.},
+@article{mediaeval00313,
+ author = {N Kini},
+ title = {TCSL at the MediaEval 2014 C@merata Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
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- author = {M Zaharieva and B Ionescu and AL Gînsca and RLT Santos and H M{\"u}ller},
- title = {Retrieving Diverse Social Images at MediaEval 2017: Challenges, Dataset and Evaluation.},
+@article{mediaeval00314,
+ author = {P Korshunov and T Ebrahimi},
+ title = {Mediaeval 2014 visual privacy task: geometrical privacy protection tool},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00573,
- author = {G Petkos and M Schinas and S Papadopoulos and Y Kompatsiaris},
- title = {Graph-based multimodal clustering for social multimedia.},
- journal = {Multimedia Tools and Applications},
- year = {2017},
+@article{mediaeval00025,
+ author = {H Mousselly-Sergieh and D Watzinger and B Huber and M Döller and E Egyed-Zsigmond and H Kosch},
+ title = {World-Wide Scale Geotagged Image Dataset for Automatic Image Annotation and Reverse Geotagging.},
+ journal = {ACM multimedia systems conference},
+ year = {2014}
}
-@article{mediaeval00574,
- author = {CH Demarty and M Sjöberg and MG Constantin and NQK Duong and B Ionescu and TT Do and H Wang},
- title = {Predicting interestingness of visual content.},
- journal = {Visual Content Indexing and Retrieval with Psycho-Visual Models},
- year = {2017},
+@article{mediaeval00037,
+ author = {A Hanbury and M Lupu and P Duygulu and A Popescu and A Iftene},
+ title = {User-Centred Image Retrieval Model.},
+ journal = {MUCKE Project Deliverable},
+ year = {2014}
}
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- author = {J Choi and M Larson and X Li and K Li and G Friedland and A Hanjalic},
- title = {The Geo-Privacy Bonus of Popular Photo Enhancements.},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2017},
+@article{mediaeval00042,
+ author = {C Gracia and X Anguera and X Binefa},
+ title = {Combining temporal and spectral information for Query-by-Example Spoken Term Detection},
+ journal = {European Signal Processing Conference (EUSIPCO)},
+ year = {2014}
}
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- author = {D Moreira and S Avila and M Perez and D Moraes and V Testoni and E Valle and S Goldenstein and A Rocha},
- title = {Temporal robust features for violence detection.},
- journal = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = {2017},
+@article{mediaeval00315,
+ author = {M Paralic and R Jarina},
+ title = {UNIZA@ Mediaeval 2014 Visual Privacy Task: Object Transparency Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00577,
- author = {S Ahmad and K Ahmad and N Ahmad and N Conci},
- title = {Convolutional Neural Networks for Disaster Images Retrieval.},
+@article{mediaeval00316,
+ author = {S Schmiedeke and P Kelm and L Goldmann and T Sikora},
+ title = {TUB@ MediaEval 2014 Visual Privacy Task: Reversible Scrambling on Foreground Masks.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00578,
- author = {L Tian and M Muszynski and C Lai and JD Moore and T Kostoulas and P Lombardo and T Pun and G Chanel},
- title = {Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?},
- journal = {IEEE International Conference on Affective Computing and Intelligent Interaction (ACII)},
- year = {2017},
+@article{mediaeval00317,
+ author = {J Hare and D Dupplaw and P Lewis and W Hall and K Martinez},
+ title = {Exploiting multimedia in creating and analysing multimedia Web archives},
+ journal = {Future Internet},
+ year = {2014},
}
-@article{mediaeval00579,
- author = {H Chen and CC Leung and L Xie and B Ma and H Li},
- title = {Multitask feature learning for low-resource query-by-example spoken term detection.},
- journal = {IEEE Journal of Selected Topics in Signal Processing},
- year = {2017},
+@article{mediaeval00318,
+ author = {C Guinaudeau and A Laurent and H Bredin},
+ title = {LIMSI@ MediaEval SED 2014.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00580,
- author = {MG Constantin and BA Boteanu and B Ionescu},
- title = {LAPI at MediaEval 2017-Predicting Media Interestingness.},
+@article{mediaeval00319,
+ author = {Z Paróczi and B Fodor and G Szücs},
+ title = {Re-Ranking the Image Search Results for Relevance and Diversity in MediaEval 2014 Challenge.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00581,
- author = {B Gündoğdu and B Yusuf and M Saraçlar},
- title = {Joint learning of distance metric and query model for posteriorgram-based keyword search},
- journal = {IEEE Journal of Selected Topics in Signal Processing},
- year = {2017},
+@article{mediaeval00320,
+ author = {V Imbrasaite and P Robinson},
+ title = {Music Emotion Tracking with Continuous Conditional Neural Fields and Relative Representation.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00582,
- author = {E Coutinho and B Schuller},
- title = {Shared acoustic codes underlie emotional communication in music and speech - Evidence from deep transfer learning},
- journal = {PloS one},
- year = {2017},
+@article{mediaeval00321,
+ author = {B Vandersmissen and A Tomar and F Godin and W De Neve and R Van de Walle},
+ title = {Ghent University-iMinds at MediaEval 2014 Diverse Images: Adaptive Clustering with Deep Features.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00583,
- author = {MC Madhavi and HA Patil},
- title = {VTLN-warped Gaussian posteriorgram for QbE-STD},
- journal = {IEEE European Signal Processing Conference (EUSIPCO)},
- year = {2017},
+@article{mediaeval00322,
+ author = {MI Sarac and P Duygulu},
+ title = {Bilkent-RETINA at retrieving diverse social images task of MediaEval 2014},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00584,
- author = {X Li and M Larson and A Hanjalic},
- title = {Geo-distinctive visual element matching for location estimation of images},
- journal = {IEEE Transactions on Multimedia},
- year = {2017},
+@article{mediaeval00323,
+ author = {M Zaharieva and P Schwab},
+ title = {A Unified Framework for Retrieving Diverse Social Images.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
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- author = {E Sansone and K Apostolidis and N Conci and G Boato and V Mezaris and FGB De Natale},
- title = {Automatic synchronization of multi-user photo galleries.},
- journal = {IEEE Transactions on Multimedia},
- year = {2017},
+@article{mediaeval00324,
+ author = {A Rosani and G Boato and FGB De Natale},
+ title = {Eventmask: a gamebased analysis of event-related saliency in photo galleries},
+ journal = {Proceedings of the IEEE ICASSP, Show and Tell},
+ year = {2014},
}
-@article{mediaeval00586,
- author = {N Tkachenko and A Zubiaga and R Procter},
- title = {Wisc at mediaeval 2017: Multimedia satellite task},
+@article{mediaeval00325,
+ author = {J Chiu and A Rudnicky},
+ title = {LACS System analysis on retrieval models for the MediaEval 2014 Search and Hyperlinking Task},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00587,
- author = {Q Gan and S Wang and L Hao and Q Ji},
- title = {A multimodal deep regression bayesian network for affective video content analyses},
- journal = {IEEE International Conference on Computer Vision},
- year = {2017},
+@article{mediaeval00326,
+ author = {M Morchid and R Dufour and G Linares},
+ title = {A Combined Thematic and Acoustic Approach for a Music Recommendation Service in TV Commercials.},
+ journal = {ISMIR},
+ year = {2014},
}
-@article{mediaeval00588,
- author = {I Gialampoukidis and A Moumtzidou and D Liparas and T Tsikrika and S Vrochidis and I Kompatsiaris},
- title = {Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression.},
- journal = {Multimedia Tools and Applications},
- year = {2017},
+@article{mediaeval00327,
+ author = {S Wan},
+ title = {A description of the CLAS system at C@merata 2014},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00589,
- author = {X Zhang and S Wang and Z Li and S Ma},
- title = {Landmark image retrieval by jointing feature refinement and multimodal classifier learning},
- journal = {IEEE transactions on cybernetics},
- year = {2017},
+@article{mediaeval00328,
+ author = {C Pantoja and E Izquierdo},
+ title = {MediaEval 2014 Visual Privacy Task: De-identification and Re-identification of Subjects in CCTV.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
}
-@article{mediaeval00590,
- author = {K Pogorelov and M Riegler and P Halvorsen and C Griwodz and T de Lange and K Randel and S Eskeland and D Nguyen and D Tien and O Ostroukhova and others},
- title = {A comparison of deep learning with global features for gastrointestinal disease detection.},
+@article{mediaeval00329,
+ author = {D Maniry and E Acar and S Albayrak},
+ title = {TUB-IRML at MediaEval 2014 Visual Privacy Task: Privacy Filtering through Blurring and Color Remapping.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00591,
- author = {N Le and H Bredin and G Sargent and P Lopez-Otero and C Barras and C Guinaudeau and G Gravier and GB da Fonseca and IL Freire and others},
- title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news.},
- journal = {ACM International Workshop on Content-Based Multimedia Indexing},
- year = {2017},
+@article{mediaeval00330,
+ author = {S Goto and T Aoki},
+ title = {Violent Scenes Detection based on Automatically-generated Mid-level Violent Concepts},
+ journal = {Computer Vision Winter Workshop},
+ year = {2014},
+}
+@article{mediaeval00331,
+ author = {J Vavrek and P Viszlay and M Lojka and M Pleva and J Juhár},
+ title = {TUKE System for MediaEval 2014 QUESST.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2014},
+}
+@article{mediaeval00332,
+ author = {M Ruocco},
+ title = {Geo-Temporal Mining and Searching of Events from Web-based Image Collections.},
+ journal = {PhD dissertation},
+ year = {2014},
+}
+@article{mediaeval00333,
+ author = {AR Simon and C Guinaudeau and P S{\'e}billot and G Gravier},
+ title = {Investigating domain-independent NLP techniques for precise target selection in video hyperlinking.},
+ journal = {ISCA/IEEE Workshop on Speech, Language and Audio in Multimedia},
+ year = {2014},
}
-@article{mediaeval00592,
- author = {X Fu and Y Bin and L Peng and J Zhou and Y Yang and HT Shen},
- title = {BMC@ MediaEval 2017 Multimedia Satellite Task via Regression Random Forest.},
+@article{mediaeval00334,
+ author = {B Safadi and M Sahuguet and B Huet},
+ title = {Linking text and visual concepts semantically for cross modal multimedia search},
+ journal = {International Conference on Image Processing (ICIP)},
+ year = {2014},
+}
+@article{mediaeval00335,
+ author = {N Kumar and SS Narayanan},
+ title = {Detection of Musical Event Drop from Crowdsourced Annotations Using a Noisy Channel Model.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00593,
- author = {E Berson and CH Demarty and N Duong},
- title = {Multimodality and deep learning when predicting media interestingness.},
+@article{mediaeval00336,
+ author = {DÓ Maidín},
+ title = {OMDN at the MediaEval 2014 C@merata Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00594,
- author = {J Parekh and H Tibrewal and S Parekh},
- title = {The IITB Predicting Media Interestingness System for MediaEval 2017.},
+@article{mediaeval00337,
+ author = {L Weiland and SP Ponzetto},
+ title = {DWS at MediaEval 2014 Retrieving Diverse Social Images: Approaches for Visual and Textual Runs.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
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- author = {O Papadopoulou and M Zampoglou and S Papadopoulos and Y Kompatsiaris},
- title = {Web video verification using contextual cues.},
- journal = {ACM International Workshop on Multimedia Forensics and Security},
- year = {2017},
+@article{mediaeval00338,
+ author = {A Terumasa and G Shinichi},
+ title = {Automatic Mid-Level Concepts Clustering for Violent Movie Scenes Detection},
+ journal = {Journal of Mathematics and System Science},
+ year = {2014},
}
-@article{mediaeval00596,
- author = {Y Liu and Z Gu and TH Ko},
- title = {Predicting Media Interestingness via Biased Discriminant Embedding and Supervised Manifold Regression.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00339,
+ author = {N Derbas and G. Quénot},
+ title = {Joint Audio-Visual Words for Violent Scenes Detection in Movies},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2014},
}
-@article{mediaeval00597,
- author = {Y Shen and CH Demarty and NQK Duong},
- title = {Deep learning for multimodal-based video interestingness prediction.},
- journal = {IEEE International Conference on Multimedia and Expo (ICME)},
- year = {2017},
+@article{mediaeval00340,
+ author = {TL Lin},
+ title = {A mutimodal framework for geocoding digital objects},
+ year = {2014},
}
-@article{mediaeval00598,
- author = {RFE Sutcliffe and Ó Maidín},
- title = {… C@merata task at MediaEval 2017: Natural Language Queries about Music, their JSON Representations, and Matching Passages in MusicXML Scores},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00341,
+ author = {W Khan},
+ title = {A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time.},
+ journal = {PhD dissertation},
+ year = {2014},
}
-@article{mediaeval00599,
- author = {S Petscharnig and K Schöffmann and M Lux},
- title = {An Inception-like CNN Architecture for GI Disease and Anatomical Landmark Classification.},
+@article{mediaeval00342,
+ author = {B Boteanu and I Mironica and AL Radu and B Ionescu},
+ title = {LAPI@ 2014 Retrieving Diverse Social Images Task: A Relevance Feedback Diversification Perspective.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
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- author = {K Koutini and A Imenina and M Dorfer and A Gruber and M Schedl},
- title = {MediaEval 2017 AcousticBrainz Genre Task: Multilayer Perceptron Approach.},
+@article{mediaeval00343,
+ author = {AR Simon and G Gravier and P S{\'e}billot and MFM IRISA},
+ title = {Hyperlinking Task},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
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- title = {Hierarchical Multilabel Classification and Voting for Genre Classification.},
+@article{mediaeval00344,
+ author = {H Karamti and M Tmar and F Gargouri},
+ title = {MIRACL's participation at MediaEval 2014 Retrieving Diverse Social Images Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
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- author = {K Nogueira and SG Fadel and ÍC Dourado and R de Oliveira Werneck and JAV Mu{\~n}oz and OAB Penatti and RT Calumby and L Li and JA dos Santos and R da Silva Torres},
- title = {Data-Driven Flood Detection using Neural Networks.},
+@article{mediaeval00345,
+ author = {W Bailer and H Stiegler},
+ title = {JRS at Search and Hyperlinking of Television Content Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2014},
}
-@article{mediaeval00603,
- author = {B Gündogdu},
- title = {Keyword search for low resource languages},
- year = {2017},
+@article{mediaeval00133,
+ author = {F Eyben and F Weninger and S Squartini and B Schuller},
+ title = {Real-life voice activity detection with lstm recurrent neural networks and an application to hollywood movies.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing},
+ year = {2013},
}
-@article{mediaeval00604,
- author = {Z Beiji and N Mohammed and Z Chengzhang and Z Rongchang},
- title = {Crime hotspot detection and monitoring using video based event modeling and mapping techniques.},
- journal = {International Journal of Computational Intelligence Systems},
- year = {2017},
+@article{mediaeval00134,
+ author = {T Reuter and S Papadopoulos and G Petkos and V Mezaris and Y Kompatsiaris and P Cimiano and C de Vries and S Geva},
+ title = {Social event detection at mediaeval 2013: Challenges, datasets, and evaluation},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00605,
- author = {J Almeida and LP Valem and DCG Pedronette},
- title = {A rank aggregation framework for video interestingness prediction},
- journal = {International Conference on Image Analysis and Processing},
- year = {2017},
+@article{mediaeval00135,
+ author = {F Metze and X Anguera and E Barnard and G Gravier},
+ title = {The spoken web search task at MediaEval 2012.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00606,
- author = {MG Constantin and B Ionescu},
- title = {Content description for Predicting image Interestingness},
- journal = {IEEE International Symposium on Signals, Circuits and Systems (ISSCS)},
- year = {2017},
+@article{mediaeval00136,
+ author = {M Eskevich and GJF Jones and R Aly and RJF Ordelman and S Chen and D Nadeem and C Guinaudeau and G Gravier and P S{\'e}billot and T De Nies and others},
+ title = {Multimedia information seeking through search and hyperlinking.},
+ journal = {ACM International conference on multimedia retrieval},
+ year = {2013},
}
-@article{mediaeval00607,
- author = {R Chakraborty and M Pandharipande and SK Kopparapu},
- title = {Analyzing Emotion in Spontaneous Speech},
- year = {2017},
+@article{mediaeval00137,
+ author = {X Anguera and F Metze and A Buzo and I Szoke and LJ Rodriguez-Fuentes},
+ title = {The spoken web search task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00608,
- author = {MC Madhavi and HA Patil},
- title = {Combining evidences from detection sources for query-by-example spoken term detection},
- journal = {IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},
- year = {2017},
+@article{mediaeval00138,
+ author = {H Wang and T Lee and CC Leung and B Ma and H Li},
+ title = {Using parallel tokenizers with DTW matrix combination for low-resource spoken term detection.},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing},
+ year = {2013},
}
-@article{mediaeval00609,
- author = {P Galuščáková and M Batko and J Čech and J Matas and D Nov{\'a}k and P Pecina},
- title = {Visual descriptors in methods for video hyperlinking.},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2017},
+@article{mediaeval00139,
+ author = {LJ Rodriguez-Fuentes and M Penagarikano},
+ title = {Mediaeval 2013 spoken web search task: System performance measures.},
+ journal = {Technical report},
+ year = {2013},
}
-@article{mediaeval00610,
- author = {RFE Sutcliffe and DÓ Maidín and EH Hovy},
- title = {The C@merata task at MediaEval 2017: Natural Language Queries about Music, their JSON Representations, and Matching Passages in MusicXML Scores.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00140,
+ author = {X Anguera and M Ferrarons},
+ title = {Memory efficient subsequence DTW for query-by-example spoken term detection},
+ journal = {IEEE International Conference on Multimedia and Expo (ICME)},
+ year = {2013},
}
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- author = {T Agrawal and R Gupta and S Sahu and CY Espy-Wilson},
- title = {SCL-UMD at the Medico Task-MediaEval 2017: Transfer Learning based Classification of Medical Images.},
+@article{mediaeval00141,
+ author = {A Abad and LJ Rodriguez-Fuentes and M Penagarikano and A Varona and G Bordel},
+ title = {On the calibration and fusion of heterogeneous spoken term detection systems.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00612,
- author = {Y Liu and Z Gu and WK Cheung},
- title = {HKBU at MediaEval 2017 Medico: Medical multimedia task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00142,
+ author = {KN Vavliakis and AL Symeonidis and PA Mitkas},
+ title = {Event identification in web social media through named entity recognition and topic modeling},
+ journal = {Data and Knowledge Engineering},
+ year = {2013},
}
-@article{mediaeval00613,
- author = {S Yoon},
- title = {TCNJ-CS@ MediaEval 2017 Emotional Impact of Movie Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00143,
+ author = {M Saraclar and A Sethy and B Ramabhadran and L Mangu and J Cui and X Cui and B Kingsbury and J Mamou},
+ title = {An empirical study of confusion modeling in keyword search for low resource languages.},
+ journal = {IEEE Workshop on Automatic Speech Recognition and Understanding},
+ year = {2013},
}
-@article{mediaeval00614,
- author = {M Rueben and WD Smart and CM Grimm and M Cakmak},
- title = {Privacy-Sensitive Robotics.},
- journal = {ACM/IEEE International Conference on Human-Robot Interaction},
- year = {2017},
+@article{mediaeval00144,
+ author = {O Van Laere and S Schockaert and B Dhoedt},
+ title = {Georeferencing Flickr resources based on textual meta-data},
+ journal = {Information Sciences},
+ year = {2013},
}
-@article{mediaeval00615,
- author = {L Tian and JD Moore and C Lai},
- title = {Recognizing emotions in spoken dialogue with acoustic and lexical cues},
+@article{mediaeval00145,
+ author = {C Hauff},
+ title = {A study on the accuracy of Flickr's geotag data},
journal = {Proceedings of the international ACM SIGIR},
- year = {2017},
-}
-@article{mediaeval00616,
- author = {P Lopez-Otero and LD Fernández and C Garcia-Mateo},
- title = {Compensating Gender Variability in Query-by-Example Search on Speech Using Voice Conversion.},
- journal = {INTERSPEECH},
- year = {2017},
-}
-@article{mediaeval00617,
- author = {C Baecchi and T Uricchio and M Bertini and A Del Bimbo},
- title = {Deep sentiment features of context and faces for affective video analysis},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00618,
- author = {S Baruah and R Gupta and S Narayanan},
- title = {A knowledge transfer and boosting approach to the prediction of affect in movies},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2017},
+@article{mediaeval00146,
+ author = {B Ionescu and J Schluter and I Mironica and M Schedl},
+ title = {A naive mid-level concept-based fusion approach to violence detection in hollywood movies},
+ journal = {ACM International conference on multimedia retrieval},
+ year = {2013},
}
-@article{mediaeval00619,
- author = {B Wang and M Larson},
- title = {Beyond concept detection: The potential of user intent for image retrieval},
- journal = {ACM Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes},
- year = {2017},
+@article{mediaeval00147,
+ author = {BK Bao and W Min and K Lu and C Xu},
+ title = {Social event detection with robust high-order co-clustering},
+ journal = {Proceedings of the 3rd ACM conference on …},
+ year = {2013},
}
-@article{mediaeval00620,
- author = {A Katsiavalos},
- title = {The DMUN System at the MediaEval 2017 C@merata Task.},
+@article{mediaeval00148,
+ author = {A Popescu and N Ballas},
+ title = {CEA LIST's Participation at MediaEval 2013 Placing Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00621,
- author = {JM Renders and G Csurka},
- title = {NLE@ MediaEval'17: Combining Cross-Media Similarity and Embeddings for Retrieving Diverse Social Images.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00149,
+ author = {PK Lanchantin and PJ Bell and MJ Gales and T Hain and X Liu and Y Long and J Quinnell and S Renals and O Saz and MS Seigel and P Swietojanski and PC Woodland},
+ title = {Automatic transcription of multi-genre media archives.},
+ year = {2013},
}
-@article{mediaeval00622,
- author = {AM Abdelhalim and MAM Salem},
- title = {Intelligent organization of multiuser photo galleries using sub-event detection},
- journal = {IEEE International Conference on Computer Engineering and Systems (ICCES)},
- year = {2017},
+@article{mediaeval00150,
+ author = {M Zaharieva and M Zeppelzauer and C Breiteneder},
+ title = {Automated social event detection in large photo collections.},
+ journal = {ACM International conference on multimedia retrieval},
+ year = {2013},
}
-@article{mediaeval00623,
- author = {N Karslioglu and Y Timar and AA Salah and H Kaya},
- title = {BOUN-NKU in MediaEval 2017 Emotional Impact of Movies Task.},
+@article{mediaeval00151,
+ author = {M Trevisiol and H J{\'e}gou and J Delhumeau and G Gravier},
+ title = {Retrieving geo-location of videos with a divide and conquer hierarchical multimodal approach.},
+ journal = {ACM International conference on multimedia retrieval},
+ year = {2013},
+}
+@article{mediaeval00152,
+ author = {C Chan and L Lee},
+ title = {Model-based unsupervised spoken term detection with spoken queries},
+ journal = {IEEE Transactions on Audio, Speech, and Language},
+ year = {2013},
+}
+@article{mediaeval00153,
+ author = {LJ Rodr{\'\i}guez-Fuentes and A Varona and M Penagarikano and G Bordel and M Diez},
+ title = {GTTS Systems for the SWS Task at MediaEval 2013.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00624,
- author = {Z Zhao and M Larson},
- title = {Retrieving Social Flooding Images Based on Multimodal Information},
+@article{mediaeval00154,
+ author = {TV Nguyen and MS Dao and R Mattivi and E Sansone and FGB De Natale and G Boato},
+ title = {Event Clustering and Classification from Social Media: Watershed-based and Kernel Methods.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00625,
- author = {T Chen and Y Wang and S Wang and S Chen},
- title = {Exploring Domain Knowledge for Affective Video Content Analyses},
- journal = {ACM international conference on Multimedia},
- year = {2017},
+@article{mediaeval00155,
+ author = {F Eyben and F Weninger and N Lehment and B Schuller and G Rigoll},
+ title = {Affective video retrieval: Violence detection in Hollywood movies by large-scale segmental feature extraction},
+ journal = {PloS one},
+ year = {2013},
}
-@article{mediaeval00626,
- author = {H Wang and L Yang and X Wu and J He},
- title = {A review of bloody violence in video classification},
- journal = {IEEE International Conference on the Frontiers and Advances in Data Science (FADS)},
- year = {2017},
+@article{mediaeval00156,
+ author = {T Sutanto and R Nayak},
+ title = {Admrg@ MediaEval 2013 social event detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00627,
- author = {RA Permadi and SGP Putra and C Helmiriawan and CCS Liem},
- title = {DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task.},
+@article{mediaeval00157,
+ author = {N Jain and J Hare and S Samangooei and J Preston and J Davies and D Dupplaw and PH Lewis},
+ title = {Experiments in diversifying flickr result sets.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00628,
- author = {J Almeida and RM Savii},
- title = {GIBIS at MediaEval 2017: Predicting Media Interestingness Task},
+@article{mediaeval00158,
+ author = {Q Dai and J Tu and Z Shi and YG Jiang and X Xue},
+ title = {Fudan at MediaEval 2013: Violent Scenes Detection Using Motion Features and Part-Level Attributes.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00629,
- author = {Y Ma and X Li and M Xu and J Jia and L Cai},
- title = {Multi-scale context based attention for dynamic music emotion prediction},
+@article{mediaeval00159,
+ author = {E Acar and F Hopfgartner and S Albayrak},
+ title = {Violence detection in hollywood movies by the fusion of visual and mid-level audio cues},
journal = {ACM international conference on Multimedia},
- year = {2017},
-}
-@article{mediaeval00630,
- author = {A Jan},
- title = {Deep learning based facial expression recognition and its applications},
- year = {2017},
-}
-@article{mediaeval00631,
- author = {B Boteanu and I. Mironică and B Ionescu},
- title = {Pseudo-relevance feedback diversification of social image retrieval results},
- journal = {Multimedia Tools and Applications},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00632,
- author = {N Bouhlel and G Feki and AB Ammar and CB Amar},
- title = {A hypergraph-based reranking model for retrieving diverse social images},
- journal = {International Conference on Computer Analysis of Images and Patterns},
- year = {2017},
+@article{mediaeval00160,
+ author = {C Guinaudeau and AR Simon and G Gravier and P S{\'e}billot},
+ title = {HITS and IRISA at MediaEval 2013: Search and hyperlinking task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00633,
- author = {L Cardoner Campi},
- title = {Deep learning for multimedia processing-Predicting media interestingness},
- journal = {PhD Thesis},
- year = {2017},
+@article{mediaeval00161,
+ author = {X Li and M Larson and A Hanjalic},
+ title = {Geo-visual ranking for location prediction of social images},
+ journal = {ACM International conference on multimedia retrieval},
+ year = {2013},
}
-@article{mediaeval00634,
- author = {O Seddati and N Ben-Lhachemi and S Dupont and S Mahmoudi},
- title = {UMONS@ MediaEval 2017: Diverse Social Images Retrieval.},
+@article{mediaeval00162,
+ author = {CC Tan and CW Ngo},
+ title = {The Vireo Team at MediaEval 2013: Violent Scenes Detection by Mid-level Concepts Learnt from Youtube.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00635,
- author = {RT Calumby and IBA do Carmo Araujo and FS Cordeiro and F Bertoni and SD Canuto and F Bel{\'e}m and MA Gon{\c{c}}alves and IC Dourado and J Munoz and L Li and others},
- title = {Rank Fusion and Multimodal Per-topic Adaptiveness for Diverse Image Retrieval.},
+@article{mediaeval00163,
+ author = {N Derbas and B Safadi and G Quénot},
+ title = {LIG at MediaEval 2013 Affect Task: Use of a Generic Method and Joint Audio-Visual Words.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00636,
- author = {N Dauban},
- title = {DNN in the AcousticBrainz Genre Task 2017.},
+@article{mediaeval00164,
+ author = {S Samangooei and J Hare and D Dupplaw and M Niranjan and N Gibbins and PH Lewis and J Davies and N Jain and J Preston},
+ title = {Social event detection via sparse multi-modal feature selection and incremental density based clustering.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00637,
- author = {K Kim and J Choi},
- title = {ICSI in MediaEval 2017 Multi-Genre Music Task.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+@article{mediaeval00165,
+ author = {MS Dao and G Boato and FGB De Natale and TV Nguyen},
+ title = {Jointly exploiting visual and non-visual information for event-related social media retrieval.},
+ journal = {ACM International conference on multimedia retrieval},
+ year = {2013},
}
-@article{mediaeval00638,
- author = {L Peng and Y Bin and X Fu and J Zhou and Y Yang and HT Shen},
- title = {CFM@ MediaEval 2017 Retrieving Diverse Social Images Task via Re-ranking and Hierarchical Clustering.},
+@article{mediaeval00166,
+ author = {D Rafailidis and T Semertzidis and M Lazaridis and MG Strintzis and P Daras},
+ title = {A Data-Driven Approach for Social Event Detection.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00639,
- author = {B Wang and M Larson},
- title = {Exploiting visual-based intent classifcation for Diverse Social Image Retrieval},
+@article{mediaeval00167,
+ author = {CA Bhatt and N Pappas and M Habibi and A Popescu-Belis},
+ title = {Idiap at MediaEval 2013: Search and hyperlinking task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2017},
-}
-@article{mediaeval00640,
- author = {GB Da Fonseca and IL Freire and Z Patrocínio Jr and SJF Guimar{\~a}es and G Sargent and R Sicre and G Gravier},
- title = {Tag Propagation Approaches within Speaking Face Graphs for Multimodal Person Discovery.},
- journal = {ACM International Workshop on Content-Based Multimedia Indexing},
- year = {2017},
-}
-@article{mediaeval00641,
- author = {D Rodríguez Navarro},
- title = {Multimodal Deep Learning methods for person annotation in video sequences},
- journal = {PhD Thesis},
- year = {2017},
-}
-@article{mediaeval00642,
- author = {J Zahálka},
- title = {The machine in multimedia analytics},
- year = {2017},
-}
-@article{mediaeval00643,
- author = {D Ferrés Domènech},
- title = {Knowledge-based and data-driven approaches for geographical information access},
- journal = {PhD Thesis},
- year = {2017},
-}
-@article{mediaeval00644,
- author = {E Celik},
- title = {Affective analysis of videos: detecting emotional content in real-life scenarios.},
- journal = {PhD Thesis},
- year = {2017},
-}
-@article{mediaeval00646,
- author = {F Qi and X Yang and T Zhang and C Xu},
- title = {Discriminative Multimodal Embedding for Event Classication},
- year = {2017},
+ year = {2013},
}
-@article{mediaeval00647,
- author = {T Agrawal and R Gupta and S Narayanan},
- title = {Multimodal detection of fake social media use through a fusion of classification and pairwise ranking systems.},
- journal = {IEEE European Signal Processing Conference (EUSIPCO)},
- year = {2017},
+@article{mediaeval00168,
+ author = {Á Erdélyi and T Winkler and B Rinner},
+ title = {Serious Fun: Cartooning for Privacy Protection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00648,
- author = {M Truelove},
- title = {Witnesses of events in social media.},
- journal = {PhD Thesis},
- year = {2017},
+@article{mediaeval00169,
+ author = {M Sjöberg and J Schlater and B Ionescu and M Schedl},
+ title = {FAR at MediaEval 2013 Violent Scenes Detection: Concept-based Violent Scenes Detection in Movies.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00649,
- author = {TP Thomas},
- title = {The Emotional Impact of Audio-Visual Stimuli},
- journal = {PhD Thesis},
- year = {2017},
+@article{mediaeval00170,
+ author = {M Wistuba and L Schmidt-Thieme},
+ title = {Supervised Clustering of Social Media Streams.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00650,
- author = {T Thomas and M Domínguez and R Ptucha},
- title = {Deep independent audio-visual affect analysis.},
- journal = {IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
- year = {2017},
+@article{mediaeval00171,
+ author = {M Zeppelzauer and M Zaharieva and M Del Fabro},
+ title = {Unsupervised Clustering of Social Events.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00651,
- author = {J Xue and K Eguchi},
- title = {Video Data Modeling Using Sequential Correspondence Hierarchical Dirichlet Processes},
- journal = {IEICE TRANSACTIONS on Information and Systems},
- year = {2017},
+@article{mediaeval00172,
+ author = {S Papadopoulos and E Schinas and V Mezaris and R Troncy and I Kompatsiaris},
+ title = {The 2012 social event detection dataset.},
+ journal = {ACM Multimedia Systems Conference},
+ year = {2013},
}
-@article{mediaeval00652,
- author = {S Xiang and W Rong and Z Xiong and M Gao and Q Xiong},
- title = {Visual and Audio Aware Bi-Modal Video Emotion Recognition.},
- journal = {CogSci},
- year = {2017},
+@article{mediaeval00173,
+ author = {I. Mironică and B Ionescu and P Knees and P Lambert},
+ title = {An in-depth evaluation of multimodal video genre categorization.},
+ journal = {IEEE International Workshop on Content-Based Multimedia Indexing (CBMI)},
+ year = {2013},
}
-@article{mediaeval00653,
- author = {J White and DW Oard},
- title = {Simulating Zero-Resource Spoken Term Discovery},
- journal = {ACM on Conference on Information and Knowledge Management},
- year = {2017},
+@article{mediaeval00174,
+ author = {D Corney and CJ Mart{\'\i}n and A G{\"o}ker and ES Xioufis and S Papadopoulos and Y Kompatsiaris and LM Aiello and B Thomee},
+ title = {SocialSensor: Finding Diverse Images at MediaEval 2013.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00654,
- author = {J van Hout and V Mitra and H Franco and C Bartels and D Vergyri},
- title = {Tackling unseen acoustic conditions in query-by-example search using time and frequency convolution for multilingual deep bottleneck features.},
- journal = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
- year = {2017},
+@article{mediaeval00175,
+ author = {I Söke and L Burget and F Gr{\'e}zl and L Ondel},
+ title = {BUT SWS 2013-Massive Parallel Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00655,
- author = {N Alsaedi},
- title = {Event identification in social media using classification-clustering framework.},
- journal = {PhD Thesis},
- year = {2017},
+@article{mediaeval00176,
+ author = {C Penet and CH Demarty and G Gravier and P Gros},
+ title = {Technicolor/inria team at the mediaeval 2013 violent scenes detection task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00656,
- author = {P Raghavendra Reddy and K Sri Rama Murty and B Yegnanarayana},
- title = {Representation Learning for Spoken Term Detection.},
- journal = {Pattern recognition and big data},
- year = {2017},
+@article{mediaeval00177,
+ author = {E Acar and F Hopfgartner and S Albayrak},
+ title = {Detecting violent content in Hollywood movies by mid-level audio representations},
+ journal = {IEEE International Workshop on Content-Based Multimedia Indexing (CBMI)},
+ year = {2013},
}
-@article{mediaeval00657,
- author = {MC Madhavi and HA Patil},
- title = {Two Stage Zero-resource Approaches for QbE-STD},
- journal = {IEEE International Conference on Advances in Pattern Recognition (ICAPR)},
- year = {2017},
+@article{mediaeval00178,
+ author = {M Brenner and E Izquierdo},
+ title = {MediaEval 2013: Social Event Detection, Retrieval and Classification in Collaborative Photo Collections.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00658,
- author = {V Vukotic},
- title = {Deep neural architectures for automatic representation learning from multimedia multimodal data},
- journal = {PhD Thesis},
- year = {2017},
+@article{mediaeval00179,
+ author = {J Davies and J Hare and S Samangooei and J Preston and N Jain and D Dupplaw and PH Lewis},
+ title = {Identifying the geographic location of an image with a multimodal probability density function},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00660,
- author = {Y Wang and F Ma and Z Jin and Y Yuan and G Xun and K Jha and L Su and J Gao},
- title = {Eann: Event adversarial neural networks for multi-modal fake news detection.},
- journal = {ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
- year = {2018},
+@article{mediaeval00180,
+ author = {H Wang and T Lee},
+ title = {The CUHK Spoken Web Search System for MediaEval 2013.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00661,
- author = {C Boididou and SE Middleton and Z Jin and S Papadopoulos and DT Dang-Nguyen and G Boato and Y Kompatsiaris},
- title = {Verifying information with multimedia content on twitter.},
- journal = {Multimedia Tools and Applications},
- year = {2018},
+@article{mediaeval00181,
+ author = {J Preston and J Hare and S Samangooei and J Davies and N Jain and D Dupplaw and PH Lewis},
+ title = {A unified, modular and multimodal approach to search and hyperlinking video.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00662,
- author = {R Cohendet and K Yadati and NQK Duong and CH Demarty},
- title = {Annotating, understanding, and predicting long-term video memorability.},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2018},
+@article{mediaeval00182,
+ author = {X Anguera and M Skácel and V Vorwerk and J Luque},
+ title = {The Telefonica Research Spoken Web Search System for MediaEval 2013.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00663,
- author = {K Nogueira and SG Fadel and ÍC Dourado and R de O Werneck and JAV Mu{\~n}oz and OAB Penatti and RT Calumby and LT Li and JA dos Santos and R da S Torres},
- title = {Exploiting ConvNet diversity for flooding identification.},
- journal = {IEEE Geoscience and Remote Sensing Letters},
- year = {2018},
+@article{mediaeval00183,
+ author = {T De Nies and W De Neve and E Mannens and R Van de Walle},
+ title = {Ghent University-iMinds at MediaEval 2013: An Unsupervised Named Entity-based Similarity Measure for Search and Hyperlinking.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00664,
- author = {C Boididou and S Papadopoulos and M Zampoglou and L Apostolidis and O Papadopoulou and Y Kompatsiaris},
- title = {Detection and visualization of misleading content on Twitter.},
- journal = {International Journal of Multimedia Information Retrieval},
- year = {2018},
+@article{mediaeval00184,
+ author = {D Manchon Vizuete and X Gir{\'o} Nieto},
+ title = {Upc at mediaeval 2013 social event detection task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00665,
- author = {D Ram and A Asaei and H Bourlard},
- title = {Sparse subspace modeling for query by example spoken term detection},
- journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
- year = {2018},
+@article{mediaeval00185,
+ author = {M Riegler and M Lux and C Kofler},
+ title = {Frame the Crowd: Global Visual Features Labeling boosted with Crowdsourcing Information.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00666,
- author = {Y Deldjoo and MG Constantin and B Ionescu and M Schedl and P Cremonesi},
- title = {Mmtf-14k: A multifaceted movie trailer feature dataset for recommendation and retrieval},
- journal = {ACM Multimedia Systems Conference},
- year = {2018},
+@article{mediaeval00186,
+ author = {A Bursuc and TB Zaharia},
+ title = {ARTEMIS@ MediaEval 2013: A Content-Based Image Clustering Method for Public Image Repositories.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00667,
- author = {H Squalli-Houssaini and NQK Duong and M Gwena{\"e}lle and CH Demarty},
- title = {Deep learning for predicting image memorability.},
- journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- year = {2018},
+@article{mediaeval00187,
+ author = {A Buzo and H Cucu and I Molnar and B Ionescu and C Burileanu},
+ title = {SpeeD@ MediaEval 2013: A Phone Recognition Approach to Spoken Term Detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00668,
- author = {B Murauer and G Specht},
- title = {Detecting Music Genre Using Extreme Gradient Boosting},
- journal = {Companion of the The Web Conference},
- year = {2018},
+@article{mediaeval00188,
+ author = {J Vavrek and M Pleva and M Lojka and P Viszlay and E Kiktov{\'a} and D Hl{\'a}dek and J Juh{\'a}r and M Pleva and E Kiktova and D Hladek and J Jozef},
+ title = {TUKE at MediaEval 2013 Spoken Web Search Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00669,
- author = {A Erdélyi and T Winkler and B Rinner},
- title = {Privacy protection vs. utility in visual data},
- journal = {Multimedia Tools and Applications},
- year = {2018},
+@article{mediaeval00189,
+ author = {SD Werner and NG Ward},
+ title = {Evaluating Prosody-Based Similarity Models for Information Retrieval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00670,
- author = {K Ahmad and A Sohail and N Conci and F De Natale},
- title = {A Comparative study of Global and Deep Features for the analysis of user-generated natural disaster related images.},
- journal = {IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
- year = {2018},
+@article{mediaeval00190,
+ author = {J Cao},
+ title = {Photo Set Refinement and Tag Segmentation in Georeferencing Flickr Photos.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00671,
- author = {D Ram and L Miculicich and H Bourlard},
- title = {CNN based query by example spoken term detection},
- journal = {INTERSPEECH},
- year = {2018},
+@article{mediaeval00191,
+ author = {B Vandersmissen and A Tomar and F Godin and W De Neve and R Van de Walle},
+ title = {Ghent University-iMinds at MediaEval 2013 Diverse Images: Relevance-Based Hierarchical Clustering.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {A Kirchknopf and D Slijepcevic and M Zeppelzauer and M Seidl},
- title = {Detection of road passability from social media and satellite images.},
+@article{mediaeval00192,
+ author = {A Popescu},
+ title = {CEA LIST's Participation at the MediaEval 2013 Retrieving Diverse Social Images Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- author = {A Moumtzidou and P Giannakeris and S Andreadis and A Mavropoulos and G Meditskos and I Gialampoukidis and K Avgerinakis and S Vrochidis and I Kompatsiaris},
- title = {A multimodal approach in estimating road passability through a flooded area using social media and satellite images.},
+@article{mediaeval00193,
+ author = {I Serrano and O D{\'e}niz and GB Garc{\'\i}a},
+ title = {VISILAB at MediaEval 2013: Fight Detection.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
-@article{mediaeval00674,
- author = {L Lopez-Fuentes and A Farasin and H Skinnemoen and P Garza},
- title = {Deep learning models for passability detection in flooded roads.},
+@article{mediaeval00194,
+ author = {F Garc{\'\i}a and E Sanchis and M Calvo and F Pla and LF Hurtado},
+ title = {ELiRF at MediaEval 2013: Similar Segments in Social Speech Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
-@article{mediaeval00675,
- author = {MC Madhavi and HA Patil},
- title = {Design of mixture of GMMs for Query-by-Example spoken term detection},
- journal = {Computer Speech and Language},
- year = {2018},
+@article{mediaeval00195,
+ author = {GA Levow},
+ title = {UWCL at MediaEval 2013: Similar Segments in Social Speech Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {J Vavrek and P Viszlay and M Lojka and J Juhár and M Pleva},
- title = {Weighted fast sequential DTW for multilingual audio Query-by-Example retrieval.},
- journal = {Journal of Intelligent Information Systems},
- year = {2018},
+@article{mediaeval00196,
+ author = {LT Li and J Almeida and OAB Penatti and RT Calumby and DCG Pedronette and MA Gon{\c{c}}alves and RdS Torres},
+ title = {Multimodal image geocoding: The 2013 RECOD's approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00677,
- author = {A Lommatzsch and B Kille},
- title = {Baseline Algorithms for Predicting the Interest in News based on Multimedia Data},
- journal = {Proceedings of the MediaEval Workshop},
- year = {2018},
+@article{mediaeval00197,
+ author = {C Kuoman and S Tollari and M Detyniecki},
+ title = {UPMC at MediaEval 2013: Relevance by Text and Diversity by Visual Clustering.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {Y Deldjoo},
- title = {Video recommendation by exploiting the multimedia content},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00198,
+ author = {JA Gómez and LF Hurtado and M Calvo and E Sanchis},
+ title = {ELiRF at MediaEval 2013: Spoken Web Search Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {M Larson and Z Liu and SFB Brugman and Z Zhao},
- title = {Pixel Privacy. Increasing Image Appeal while Blocking Automatic Inference of Sensitive Scene Information},
+@article{mediaeval00199,
+ author = {GV Mantena and K Prahallad},
+ title = {IIIT-H SWS 2013: Gaussian Posteriorgrams of Bottle-Neck Features for Query-by-Example Spoken Term Detection.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- author = {A Ceroni},
- title = {Personal Photo Management and Preservation},
- journal = {Personal Multimedia Preservation},
- year = {2018},
+@article{mediaeval00200,
+ author = {M Eskevich and GJF Jones},
+ title = {Time-based segmentation and use of jump-in points in DCU search runs at the Search and Hyperlinking task at MediaEval 2013},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {SK Singh and D Rafiei},
- title = {Strategies for geographical scoping and improving a gazetteer},
- journal = {ACM International Conference on World Wide Web},
- year = {2018},
+@article{mediaeval00201,
+ author = {K Aoyama and A Ogawa and T Hattori and T Hori and A Nakamura},
+ title = {Graph index based query-by-example search on a large speech data set},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2013},
}
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- author = {Y Liu and Z Gu and TH Ko and KA Hua},
- title = {Learning perceptual embeddings with two related tasks for joint predictions of media interestingness and emotions},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2018},
+@article{mediaeval00202,
+ author = {P Galuščáková and P Pecina},
+ title = {CUNI at MediaEval 2013 Similar Segments in Social Speech Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- title = {Deep learning approaches for flood classification and flood aftermath detection.},
+@article{mediaeval00203,
+ author = {A Ali and M Clements},
+ title = {Spoken Web Search using an Ergodic Hidden Markov Model of Speech.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- author = {MS Dao and P Quang Nhat Minh and A Kasem and NS Haja Nazmudeen},
- title = {A Context-Aware Late-Fusion Approach for Disaster Image Retrieval from Social Media},
- journal = {International Conference on Multimedia Retrieval},
- year = {2018},
+@article{mediaeval00204,
+ author = {M Bouallegue and G Senay and M Morchid and D Matrouf and G Linar{\`e}s and R Dufour},
+ title = {LIA@ MediaEval 2013 Spoken Web Search Task: An I-Vector based Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- title = {Extracting semantic knowledge from web context for multimedia IR: a taxonomy, survey and challenges.},
- journal = {Multimedia Tools and Applications},
- year = {2018},
+@article{mediaeval00205,
+ author = {P Galuščáková and P Pecina},
+ title = {CUNI at MediaEval 2012 Search and Hyperlinking Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {O Ben-Ahmed and B Huet},
- title = {Deep Multimodal Features for Movie Genre and Interestingness Prediction},
- journal = {IEEE International Conference on Content-Based Multimedia Indexing (CBMI)},
- year = {2018},
+@article{mediaeval00206,
+ author = {C Ventura and M Tella-Amo and XG i Nieto},
+ title = {UPC at MediaEval 2013 Hyperlinking Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {J Parekh and H Tibrewal and S Parekh},
- title = {Deep pairwise classification and ranking for predicting media interestingness},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2018},
+@article{mediaeval00207,
+ author = {M Morchid and R Dufour and G Linares},
+ title = {Event detection from image hosting services by slightly-supervised multi-span context models},
+ journal = {International Workshop on Content-Based Multimedia Indexing (CBMI)},
+ year = {2013},
}
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- title = {Interestingness Prediction and its Application to Immersive Content.},
- journal = {IEEE International Conference on Content-Based Multimedia Indexing (CBMI)},
- year = {2018},
+@article{mediaeval00208,
+ author = {M Lokaj and H Stiegler and W Bailer},
+ title = {TOSCA-MP at Search and Hyperlinking of Television Content Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {C Maigrot and V Claveau and E Kijak},
- title = {Fusion-based multimodal detection of hoaxes in social networks},
- journal = {IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
- year = {2018},
+@article{mediaeval00209,
+ author = {K Schouten and R Aly and R Ordelman},
+ title = {Searching and Hyperlinking using Word Importance Segment Boundaries in MediaEval 2013.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {I Serrano and O Deniz and JL Espinosa-Aranda and G Bueno},
- title = {Fight Recognition in Video Using Hough Forests and 2D Convolutional Neural Network.},
- journal = {IEEE Transactions on Image Processing},
- year = {2018},
+@article{mediaeval00210,
+ author = {P Knees and M Schedl},
+ title = {Music similarity and retrieval},
+ journal = {Proceedings of the international ACM SIGIR},
+ year = {2013},
}
-@article{mediaeval00691,
- author = {J Xue and K Eguchi},
- title = {Supervised nonparametric multimodal topic modeling methods for multi-class video classification},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2018},
+@article{mediaeval00211,
+ author = {A Armagan and A Popescu and P Duygulu},
+ title = {Mucke participation at retrieving diverse social images task of mediaeval 2013.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {J Xue and K Eguchi},
- title = {Sequential Bayesian nonparametric multimodal topic models for video data analysis},
- journal = {IEICE Transactions on Information and Systems},
- year = {2018},
+@article{mediaeval00212,
+ author = {S Goto and T Aoki},
+ title = {TUDCL at MediaEval 2013 Violent Scenes Detection: Training with Multi-modal Features by MKL.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00693,
- author = {X Zhang and Z Zhao and H Zhang and S Wang and Z Li},
- title = {Unsupervised geographically discriminative feature learning for landmark tagging},
- journal = {Knowledge-Based Systems},
- year = {2018},
+@article{mediaeval00213,
+ author = {V Lam and S Phan and TD Ngo and DD Le and DA Duong and S Satoh},
+ title = {Violent scene detection using mid-level feature.},
+ journal = {Proceedings of the Fourth Symposium on Information and Communication Technology},
+ year = {2013},
}
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- author = {G Palaiokrassas and A Voulodimos and A Litke and A Papaoikonomou and T Varvarigou},
- title = {A Distance-Dependent Chinese Restaurant Process Based Method for Event Detection on Social Media.},
- journal = {Inventions},
- year = {2018},
+@article{mediaeval00214,
+ author = {A Sheshadri and M Lease},
+ title = {SQUARE: Benchmarking Crowd Consensus at MediaEval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00695,
- author = {SFB Brugman and M Wysokinski and M Larson},
- title = {Exploring Three Views on Image Enhancement for Pixel Privacy},
+@article{mediaeval00215,
+ author = {J Hare and M Acosta and A Weston and E Simperl and S Samangooei and D Dupplaw and PH Lewis},
+ title = {An investigation of techniques that aim to improve the quality of labels provided by the crowd.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
-@article{mediaeval00696,
- author = {V Thambawita and D Jha and M Riegler and P Halvorsen and HL Hammer and HD Johansen and D Johansen},
- title = {The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning.},
+@article{mediaeval00216,
+ author = {A Melle and JL Dugelay},
+ title = {Shape and Color-aware Privacy Protection.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- author = {A Ciobanu and A Lommatzsch and B Kille},
- title = {Predicting the Interest in News based On Image Annotations},
+@article{mediaeval00217,
+ author = {HJ Escalante and A Morales-Reyes},
+ title = {TIA-INAOE's Approach for the 2013 Retrieving Diverse Social Images Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- title = {VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability.},
- journal = {arXiv preprint},
- year = {2018},
+@article{mediaeval00218,
+ author = {AL Radu and B Boteanu and O Ples and B Ionescu},
+ title = {LAPI@ Retrieving Diverse Social Images Task 2013: Qualitative Photo Retrieval using Multimedia Content.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {M Rohm and B Ionescu and AL Gînscă and RLT Santos and H M{\"u}ller},
- title = {Subdiv17: a dataset for investigating subjectivity in the visual diversification of image search results.},
- journal = {ACM Multimedia Systems Conference},
- year = {2018},
+@article{mediaeval00219,
+ author = {R Jarina and M Kuba and R Gubka and M Chmulik and M Paralic},
+ title = {UNIZA System for the Spoken Web Search Task at MediaEval2013.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00700,
- author = {S Oramas and D Bogdanov and A Porter},
- title = {MediaEval 2018 AcousticBrainz Genre Task: A baseline combining deep feature embeddings across datasets},
+@article{mediaeval00220,
+ author = {S Magliacane and PT Groth},
+ title = {Repurposing Benchmark Corpora for Reconstructing Provenance.},
+ journal = {SePublica},
+ year = {2013},
+}
+@article{mediaeval00221,
+ author = {M Georgescu and X Zhu},
+ title = {L3S at MediaEval 2013 Crowdsourcing for Social Multimedia Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- author = {S Nadeem and MA Tahir and SSA Naqvi and M Zaid},
- title = {Ensemble of Texture and Deep Learning Features for Finding Abnormalities in the Gastro-Intestinal Tract},
- journal = {International Conference on Computational Collective Intelligence},
- year = {2018},
+@article{mediaeval00222,
+ author = {D Maniry and E Acar and S Albayrak},
+ title = {DAI at the MediaEval 2013 Visual Privacy Task: Representing People with Foreground Edges.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
+}
+@article{mediaeval00223,
+ author = {V Lam and DD Le and S Phan and S Satoh and DA Duong and TD Ngo},
+ title = {Evaluation of Low-Level features for detecting Violent Scenes in videos},
+ journal = {International Conference on Soft Computing and Pattern Recognition (SoCPaR)},
+ year = {2013},
+}
+@article{mediaeval00224,
+ author = {I Tankoyeu and J Stöttinger and F Giunchiglia},
+ title = {Context-based media geotagging of personal photos},
+ journal = {Technical report},
+ year = {2013},
+}
+@article{mediaeval00225,
+ author = {M Brenner and E Izquierdo},
+ title = {Event-driven retrieval in collaborative photo collections},
+ journal = {International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)},
+ year = {2013},
+}
+@article{mediaeval00226,
+ author = {M Morchid and R Dufour and M Bouallegue and G Linarès and D Matrouf},
+ title = {LIA@ MediaEval 2013 MusiClef Task: A Combined Thematic and Acoustic Approach.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00702,
- author = {M Taschwer and MJ Primus and K Schoeffmann and O Marques},
- title = {Early and Late Fusion of Classifiers for the MediaEval Medico Task},
+@article{mediaeval00227,
+ author = {M Morchid and R Dufour and M Bouallegue and G Linarès and D Matrouf},
+ title = {LIA@ MediaEval 2013 Crowdsourcing Task: Metadata or not Metadata? That is a Fashion Question.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
-@article{mediaeval00703,
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- title = {Dublin's participation in the predicting media memorability task at MediaEval 2018.},
+@article{mediaeval00228,
+ author = {F Garc{\'\i}a Granada and E Sanch{\'\i}s Arnal and M Calvo Lance and F Pla Santamar{\'\i}a and LF Hurtado Oliver},
+ title = {ELIRF at MEDIAEVAL 2013: Similar Segments of Social Speech Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
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- author = {P Galuščáková},
- title = {Information retrieval and navigation in audio-visual archives},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00229,
+ author = {MH Sedky and CC Chibelushi and M Moniri},
+ title = {MediaEval 2013 Visual Privacy Task: Physics-Based Technique for Protecting Privacy in Surveillance Videos.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
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- author = {J Cao},
- title = {Large-scale content management and retrieval for social media},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00230,
+ author = {M Sedky and CC Chibelushi and M Moniri},
+ title = {Physics-Based Technique for Protecting Privacy in Surveillance Videos},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00706,
- author = {Y Yi and H Wang},
- title = {Multi-modal learning for affective content analysis in movies},
- journal = {Multimedia Tools and Applications},
- year = {2018},
+@article{mediaeval00231,
+ author = {S Schmiedeke and P Kelm and T Sikora},
+ title = {TUB@ MediaEval 2013 Visual Privacy Task: Reversible Scrambling with colour-preservative Characteristic.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00707,
- author = {Z Zhao and MA Larson and NHJ Oostdijk},
- title = {Exploiting Local Semantic Concepts for Flooding-related Social Image Classification},
+@article{mediaeval00232,
+ author = {B do Nascimento Teixeira},
+ title = {MTM at MediaEval 2013 Violent Scenes Detection: Through Acoustic-visual Transform.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018},
+ year = {2013},
}
-@article{mediaeval00708,
- author = {S Sarman and M Sert},
- title = {Audio based violent scene classification using ensemble learning},
- journal = {IEEE International Symposium on Digital Forensic and Security (ISDFS)},
- year = {2018},
+@article{mediaeval00233,
+ author = {C Gracia and X Anguera and X Binefa},
+ title = {The CMTECH Spoken Web Search System for MediaEval},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00709,
- author = {RDO Werneck and IC Dourado and SG Fadel and S Tabbone and RdaS Torres},
- title = {Graph-Based Early-Fusion for Flood Detection.},
- journal = {IEEE International Conference on Image Processing (ICIP)},
- year = {2018},
+@article{mediaeval00234,
+ author = {C Wartena},
+ title = {Segmentation Strategies for Passage Retrieval from Internet Video using Speech Transcripts},
+ journal = {Journal of Digital Information Management},
+ year = {2013},
}
-@article{mediaeval00710,
- author = {X Cheng and X Zhang and M Xu and TF Zheng},
- title = {MMANN: Multimodal Multilevel Attention Neural Network for Horror Clip Detection.},
- journal = {IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},
- year = {2018},
+@article{mediaeval00235,
+ author = {C Pantoja and VF Arguedas and E Izquierdo},
+ title = {MediaEval 2013 Visual Privacy Task: Pixel Based Anonymisation Technique.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013},
}
-@article{mediaeval00711,
- author = {TJ Bracamonte Nole},
- title = {Improving web multimedia information retrieval using social data},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00028,
+ author = {K Yanai and DH Nga},
+ title = {UEC, Tokyo at MediaEval 2013 Retrieving Diverse Social Images Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013}
}
-@article{mediaeval00712,
- author = {X Zhang and X Cheng and M Xu and TF Zheng},
- title = {Imbalance Learning-based Framework for Fear Recognition in the MediaEval Emotional Impact of Movies Task},
- journal = {Interspeech},
- year = {2018},
+@article{mediaeval00048,
+ author = {K Tserpes and M Kardara and T Varvarigou},
+ title = {A similarity-based Chinese Restaurant Process for Social Event Detection},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013}
}
-@article{mediaeval00713,
- author = {MR Khokher},
- title = {Audio-visual Video Recognition Through Super Descriptor Tensor Decomposition and Low-rank and Sparse Representation},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00049,
+ author = {JLR Garcia and R Troncy and L Pikora},
+ title = {LinkedTV at MediaEval 2013 Search and Hyperlinking Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2013}
}
-@article{mediaeval00714,
- author = {B Zou and M Nurudeen and C Zhu and Z Zhang and R Zhao and L Wang},
- title = {A Neuro-Fuzzy Crime Prediction Model Based on Video Analysis},
- journal = {Chinese Journal of Electronics},
- year = {2018},
+@article{mediaeval00236,
+ author = {M Ruocco and H Ramampiaro},
+ title = {Event-related image retrieval: exploring geographical and temporal distribution of user tags},
+ journal = {International Journal of Multimedia Information Retrieval},
+ year = {2013},
}
-@article{mediaeval00715,
- author = {S Wang and S Chen and J Zhao and Q Jin},
- title = {Video Interestingness Prediction Based on Ranking Model},
- journal = {ACM Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and first Multi-Modal Affective Computing of Large-Scale Multimedia Data},
- year = {2018},
+@article{mediaeval00237,
+ author = {E Apostolidis and V Mezaris and CD Stein and S Eickeler},
+ title = {Television Linked To The Web},
+ year = {2013},
}
-@article{mediaeval00716,
- author = {AM Atto and A Benoît and P Lambert},
- title = {Hilbert Based Video To Timed-Image Filling and Learning To Recognize Visual Violence},
- year = {2018},
+@article{mediaeval00088,
+ author = {G Petkos and S Papadopoulos and Y Kompatsiaris},
+ title = {Social event detection using multimodal clustering and integrating supervisory signals.},
+ journal = {Proceedings of the ACM International Conference on Multimedia Retrieval},
+ year = {2012},
}
-@article{mediaeval00717,
- author = {C Maigrot and E Kijak and V Claveau},
- title = {Fusion par apprentissage pour la détection de fausses informations dans les réseaux sociaux},
- journal = {Document numerique},
- year = {2018},
+@article{mediaeval00089,
+ author = {F Metze and E Barnard and M Davel and C Van Heerden and X Anguera and G Gravier and N Rajput},
+ title = {The spoken web search task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2012},
}
-@article{mediaeval00718,
- author = {Y Timar and N Karslioglu and H Kaya and AA Salah},
- title = {Feature Selection and Multimodal Fusion for Estimating Emotions Evoked by Movie Clips},
- journal = {ACM International Conference on Multimedia Retrieval},
- year = {2018},
+@article{mediaeval00090,
+ author = {C Hauff and GJ Houben},
+ title = {Placing images on the world map: a microblog-based enrichment approach},
+ journal = {Proceedings of the international ACM SIGIR},
+ year = {2012},
}
-@article{mediaeval00719,
- author = {M Muszynski},
- title = {Recognizing film aesthetics, spectators' affect and aesthetic emotions from multimodal signals},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00091,
+ author = {M Brenner and E Izquierdo},
+ title = {Social event detection and retrieval in collaborative photo collections},
+ journal = {Proceedings of the ACM International Conference on Multimedia Retrieval},
+ year = {2012},
}
-@article{mediaeval00720,
- author = {C Hou and X Wu and G Wang},
- title = {End-to-End Bloody Video Recognition by Audio-Visual Feature Fusion},
- journal = {Chinese Conference on Pattern Recognition and Computer Vision},
- year = {2018},
+@article{mediaeval00092,
+ author = {OAB Penatti and LT Li and J Almeida and R da S Torres},
+ title = {A visual approach for video geocoding using bag-of-scenes},
+ journal = {Proceedings of the ACM International Conference on Multimedia Retrieval},
+ year = {2012},
}
-@article{mediaeval00721,
- author = {NR Nandigam},
- title = {MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00093,
+ author = {C Penet and CH Demarty and G Gravier and P Gros},
+ title = {Multimodal information fusion and temporal integration for violence detection in movies},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2012},
}
-@article{mediaeval00722,
- author = {A Khwileh},
- title = {Towards effective cross-lingual search of user-generated internet speech},
- journal = {PhD Thesis},
- year = {2018},
+@article{mediaeval00094,
+ author = {Y Wang and H Sundaram and L Xie},
+ title = {Social event detection with interaction graph modeling},
+ journal = {Proceedings of the ACM international conference on Multimedia},
+ year = {2012},
}
-@article{mediaeval00001,
- author = {RJ Borgli and P Halvorsen and M Riegler and HK Stensland},
- title = {Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task},
+@article{mediaeval00095,
+ author = {A Abad and RF Astudillo and I Trancoso},
+ title = {The L2F Spoken Web Search system for Mediaeval 2012.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00002,
- author = {M Lux and M Riegler and DT Dang-Nguyen and M Larson and M Potthast and P Halvorsen},
- title = {Team ORG@ GameStory Task 2018.},
+@article{mediaeval00096,
+ author = {E Schinas and G Petkos and S Papadopoulos and Y Kompatsiaris},
+ title = {CERTH@ MediaEval 2012 Social Event Detection Task.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00007,
- author = {S Wang and W Wang and S Chen and Q Jin},
- title = {RUC at MediaEval 2018: Visual and Textual Features Exploration for Predicting Media Memorability},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00046,
+ author = {AL Baulida},
+ title = {Semantic and Diverse Summarization of Egocentric Photo Events},
+ journal = {M. Sc. Visions and Robotics Thesis},
+ year = {2012}
}
-@article{mediaeval00003,
- author = {M Wutti},
- title = {Automated Killstreak Extraction in CS: GO Tournaments},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00097,
+ author = {C Hauff and GJ Houben},
+ title = {Geo-location estimation of flickr images: Social web based enrichment},
+ journal = {European Conference on Information Retrieval},
+ year = {2012},
}
-@article{mediaeval00004,
- author = {R Gupta and K Motwani},
- title = {Linear Models for Video Memorability Prediction Using Visual and Semantic Features},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00098,
+ author = {H Lei and J Choi and G Friedland},
+ title = {Multimodal city-verification on flickr videos using acoustic and textual features},
+ journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year = {2012},
+}
+@article{mediaeval00099,
+ author = {J Choi and G Friedland and V Ekambaram and K Ramchandran},
+ title = {Multimodal location estimation of consumer media: Dealing with sparse training data},
+ journal = {IEEE International Conference on Multimedia and Expo},
+ year = {2012},
}
-@article{mediaeval00006,
- author = {Y Liu and Z Gu and TH Ko},
- title = {Analyzing Human Behavior in Subspace: Dimensionality Reduction+ Classification},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00100,
+ author = {M Eskevich and GJF Jones and C Wartena and M Larson and R Aly and T Verschoor and R Ordelman},
+ title = {Comparing retrieval effectiveness of alternative content segmentation methods for internet video search.},
+ journal = {International Workshop on Content-Based Multimedia Indexing},
+ year = {2012},
}
-@article{mediaeval00008,
- author = {L Cabrera-Quiros and E Gedik and H Hung},
- title = {Transductive Parameter Transfer, Bags of Dense Trajectories and MILES for No-Audio Multimodal Speech Detection},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00101,
+ author = {GJF Jones},
+ title = {An introduction to crowdsourcing for language and multimedia technology research},
+ journal = {PROMISE Winter School},
+ year = {2012},
}
-@article{mediaeval00009,
- author = {SA Hicks and PH Smedsrud and P Halvorsen and M Riegler},
- title = {Deep Learning Based Disease Detection Using Domain Specific Transfer Learning},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00102,
+ author = {C Wartena},
+ title = {Comparing segmentation strategies for efficient video passage retrieval},
+ journal = {IEEE International Workshop on Content-Based Multimedia Indexing (CBMI)},
+ year = {2012},
}
-@article{mediaeval00011,
- author = {TH Ko and Z Gu and Y Liu},
- title = {Weighted Discriminant Embedding: Discriminant Subspace Learning for Imbalanced Medical Data Classification},
+@article{mediaeval00103,
+ author = {LT Li and J Almeida and DCG Pedronette and OAB Penatti and R da S Torres},
+ title = {A multimodal approach for video geocoding.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00012,
- author = {W Sun and X Zhang},
- title = {Video Memorability Prediction with Recurrent Neural Networks and Video Titles at the 2018 MediaEval Predicting Media Memorability Task},
+@article{mediaeval00104,
+ author = {P Kelm and S Schmiedeke and T Sikora},
+ title = {How Spatial Segmentation improves the Multimodal Geo-Tagging.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00013,
- author = {F Nazary and Y Deldjoo},
- title = {Movie Rating Prediction using Multimedia Content and Modeling as a Classification Problem},
+@article{mediaeval00105,
+ author = {N Derbas and F Thollard and B Safadi and G Quénot},
+ title = {Lig at mediaeval 2012 affect task: use of a generic method.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00014,
- author = {T Joshi and S Sivaprasad and S Bhat and N Pedanekar},
- title = {Multimodal Approach to Predicting Media Memorability},
+@article{mediaeval00106,
+ author = {J Schlater and B Ionescu and I Mironica and M Schedl},
+ title = {ARF@ MediaEval 2012: An Uninformed Approach to Violence Detection in Hollywood Movies.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00015,
- author = {Z Khan and MA Tahir},
- title = {Majority voting of Heterogeneous Classifiers for finding abnormalities in the Gastro-Intestinal Tract},
+@article{mediaeval00107,
+ author = {F Eyben and F Weninger and N Lehment and G Rigoll and B Schuller},
+ title = {Violent scenes detection with large, brute-forced acoustic and visual feature sets.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00016,
- author = {H Schreiber},
- title = {MediaEval 2018 AcousticBrainz Genre Task: A CNN Baseline Relying on Mel-Features},
+@article{mediaeval00108,
+ author = {M Zeppelzauer and M Zaharieva and C Breiteneder},
+ title = {A Generic Approach for Social Event Detection in Large Photo Collections.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00017,
- author = {D Dias and U Dias},
- title = {Transfer learning with CNN architectures for classifying gastrointestinal diseases and anatomical landmarks},
+@article{mediaeval00109,
+ author = {M Brenner and E Izquierdo},
+ title = {QMUL@ MediaEval 2012: Social Event Detection in Collaborative Photo Collections.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00018,
- author = {O Ostroukhova and K Pogorelov and M Riegler and DT Dang-Nguyen and P Halvorsen},
- title = {Transfer learning with prioritized classification and training dataset equalization for medical objects detection.},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00110,
+ author = {LT Li and DCG Pedronette and J Almeida and OA Penatti and RT Calumby and R da S Torres},
+ title = {Multimedia multimodal geocoding},
+ journal = {ACM International Conference on Advances in Geographic Information Systems},
+ year = {2012},
}
-@article{mediaeval00019,
- author = {Y Liu and Z Gu and TH Ko},
- title = {Learning Memorability Preserving Subspace for Predicting Media Memorability},
+@article{mediaeval00111,
+ author = {E Acar and S Albayrak},
+ title = {DAI Lab at MediaEval 2012 Affect Task: The Detection of Violent Scenes using Affective Features.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00020,
- author = {DT Tran-Van and LV Tran and MT Tran},
- title = {Predicting Media Memorability Using Deep Features and Recurrent Network},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00112,
+ author = {M Eskevich and GJF Jones and M Larson and R Ordelman},
+ title = {Creating a data collection for evaluating rich speech retrieval},
+ journal = {International conference on Language Resources and Evaluation (LREC)},
+ year = {2012},
}
-@article{mediaeval00021,
- author = {A Weiss and B Sang and S Yoon},
- title = {Predicting Memorability via Early Fusion Deep Neural Network},
+@article{mediaeval00113,
+ author = {X Li and C Hauff and M Larson and A Hanjalic},
+ title = {Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012}
}
-@article{mediaeval00022,
- author = {RM Savii and SF dos Santos and J Almeida},
- title = {GIBIS at MediaEval 2018: Predicting Media Memorability Task},
+@article{mediaeval00114,
+ author = {A Varona and M Penagarikano and LJ Rodríguez-Fuentes and G Bordel and M Diez},
+ title = {GTTS System for the Spoken Web Search Task at MediaEval 2012.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00023,
- author = {R Chaudhry and M Kilaru and S Shekhar},
- title = {Show and Recall@ MediaEval 2018 ViMemNet: Predicting Video Memorability},
+@article{mediaeval00115,
+ author = {MS Dao and TV Nguyen and G Boato and FGB De Natale},
+ title = {The Watershed-based Social Events Detection Method with Support from External Data Sources.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00024,
- author = {R Cohendet and CH Demarty and NQK Duong},
- title = {Transfer learning for video memorability prediction},
+@article{mediaeval00116,
+ author = {YG Jiang and Q Dai and CC Tan and X Xue and CW Ngo},
+ title = {The Shanghai-Hongkong Team at MediaEval2012: Violent Scene Detection Using Trajectory-based Features.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00026,
- author = {TH Ko and Z Gu and T He and Y Liu},
- title = {Towards Learning Emotional Subspace},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00117,
+ author = {S Schmiedeke and P Kelm and T Sikora},
+ title = {Cross-modal categorisation of user-generated video sequences},
+ journal = {ACM International Conference on Multimedia Retrieval},
+ year = {2012},
}
-@article{mediaeval00027,
- author = {C Loughridge and J Moseyko},
- title = {IM-JAIC at MediaEval 2018 Emotional Impact of Movies Task},
+@article{mediaeval00118,
+ author = {C Penet and CH Demarty and M Soleymani and G Gravier and P Gros},
+ title = {Technicolor/inria/imperial college london at the mediaeval 2012 violent scene detection task},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00029,
- author = {JJ Sun and T Liu and G Prasad},
- title = {GLA in MediaEval 2018 Emotional Impact of Movies Task},
+@article{mediaeval00119,
+ author = {O Van Laere and S Schockaert and J Quinn and FC Langbein and B Dhoedt},
+ title = {Ghent and cardiff university at the 2012 placing task},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00030,
- author = {M Hanif and MA Tahir and M Rafi},
- title = {Detection of passable roads using Ensemble of Global and Local},
+@article{mediaeval00120,
+ author = {J Choi and VN Ekambaram and G Friedland and K Ramchandran},
+ title = {The 2012 ICSI/Berkeley Video Location Estimation System.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
-}
-@article{mediaeval00031,
- author = {P Lopez-Otero and L Docio-Fernandez},
- title = {Converted Mel-Cepstral Coefficients for Gender Variability Reduction in Query-by-Example Spoken Document Retrieval},
- journal = {isca-speech.org},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00032,
- author = {E Batziou and E Michail and K Avgerinakis and S Vrochidis and I Patras and I Kompatsiaris},
- title = {Visual and audio analysis of movies video for emotion detection@ Emotional Impact of Movies task MediaEval 2018.},
+@article{mediaeval00121,
+ author = {T De Nies and P Debevere and D Van Deursen and W De Neve and E Mannens and R Van de Walle},
+ title = {Ghent University-IBBT at MediaEval 2012 Search and Hyperlinking: Semantic Similarity using Named Entities.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+ year = {2012},
}
-@article{mediaeval00033,
- author = {D Dias and U Dias},
- title = {Flood detection from social multimedia and satellite images using ensemble and transfer learning with CNN architectures},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00122,
+ author = {S Diplaris and G Petkos and S Papadopoulos and Y Kompatsiaris and N Sarris and C Martin and A Goker and D Corney and J Geurts and Y Liu and JC Point},
+ title = {Social sensor: Surfacing real-time trends and insights from multiple social networks.},
+ journal = {NEM Summit Proceedings},
+ year = {2012},
}
-@article{mediaeval00034,
- author = {B Bischke and P Helber and A Dengel},
- title = {Global-Local Feature Fusion for Image Classification of Flood Affected Roads from Social Multimedia},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00123,
+ author = {OAB Penatti and E Valle and RS Torres},
+ title = {Image and video representations based on visual dictionaries},
+ journal = {Ph. D. Thesis, Universidade Estadual de Campinas},
+ year = {2012},
}
-@article{mediaeval00039,
- author = {Y Yi and H Wang and Q Li},
- title = {CNN Features for Emotional Impact of Movies Task},
- journal = {MediaEval Working Notes Proceedings},
- year = {2018}
+@article{mediaeval00124,
+ author = {J Fleureau and C Penet and P Guillotel and CH Demarty},
+ title = {Electrodermal activity applied to violent scenes impact measurement and user profiling.},
+ journal = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
+ year = {2012},
}
-@article{mediaeval00041,
- author = {YM Yang},
- title = {Short-time emotion tracker using a convolutional autoencoder},
- year = {2018}
+@article{mediaeval00125,
+ author = {M Ruocco},
+ title = {Context-aware image semantic extraction in the social web},
+ journal = {ACM International Conference on World Wide Web},
+ year = {2012},
}
-@article{mediaeval00035,
- author = {KAC Quan and VT Nguyen and MT Tran},
- title = {Frame-based Evaluation with Deep Features to Predict Emotional Impact of Movies},
+@article{mediaeval00126,
+ author = {D Nadeem and R Aly and R Ordelman},
+ title = {UTwente does Brave New Tasks for MediaEval 2012: Searching and Hyperlinking.},
journal = {MediaEval Working Notes Proceedings},
- year = {2018}
-}
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- author = {Y Feng and S Shebotnov and C Brenner and M Sester},
- title = {Ensembled convolutional neural network models for retrieving flood relevant tweets},
- journal = {Image},
- year = {2018},
+ year = {2012},
}
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-@inproceedings{ramirez2018overview,
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- booktitle={International Conference on Pattern Recognition},
- pages={127--139},
- year={2018},
- organization={Springer}
+@article{mediaeval00127,
+ author = {A Badii and M Einig},
+ title = {MediaEval 2012 Visual Privacy Task: Privacy, Intelligibility through Pixellation and Edge Detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2012},
}
-
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- author={Z Yang and Q Li and L Wenyin and J Lv},
- journal={IEEE transactions on pattern analysis and machine intelligence},
- year={2019},
- publisher={IEEE}
+@article{mediaeval00128,
+ author = {JC Lei and G Friedland},
+ title = {Multimodal city-identification on flickr videos using acoustic and textual features},
+ journal = {Proceedings of the ACM international conference on Multimedia},
+ year = {2012},
}
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- booktitle={2018 Digital Image Computing: Techniques and Applications (DICTA)},
- pages={1--8},
- year={2018},
- organization={IEEE}
+@article{mediaeval00129,
+ author = {AI Oviedo and O Ortega and JM Perea-Ortega and E Sanchis},
+ title = {Video clustering based on the collaboration of multimedia clusterers.},
+ journal = {Conferencia Latinoamericana En Informatica (CLEI)},
+ year = {2012},
}
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- title={Short-time Emotion Tracker Using a Convolutional Autoencoder},
- author={Y mo Yang},
- journal={ECE Department at University of Rochester},
- year={2019}
+@article{mediaeval00130,
+ author = {M Florian and R Nitendra and A Xavier and D Marelie and ...},
+ title = {The spoken web search task at MediaEval 2011},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2012},
}
-
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- title={Music Emotion Recognition Using a Variant of Recurrent Neural Network},
- author={H Liu and Y Fang and Q Huang},
- booktitle={2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application (MMSSA 2018)},
- year={2019},
- organization={Atlantis Press}
+@article{mediaeval00131,
+ author = {M Eskevich and GJF Jones},
+ title = {DCU search runs at MediaEval 2012: search and hyperlinking task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2012},
}
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- title={System, method, and computer program for estimation of target value},
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- year={2019},
- booktitle={US Patent App. 16/070,144},
+@article{mediaeval00132,
+ author = {J Choi},
+ title = {A Multimodal Approach to Automatic Geo-Tagging of Video},
+ year = {2012},
}
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- journal={Information Fusion},
- volume={51},
- pages={224--232},
- year={2019},
- publisher={Elsevier}
+@article{mediaeval00054,
+ author = {J Choi and A Janin and G Friedland},
+ title = {The 2010 ICSI video location estimation system},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- author={K Markov and T Matsui},
- journal={IEEE access},
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- year={2014},
- publisher={IEEE}
+@article{mediaeval00055,
+ author = {O Van Laere and S Schockaert and B Dhoedt},
+ title = {Ghent university at the 2011 Placing Task},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
-
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- journal={IEEE Transactions on Affective Computing},
- year={2019},
- publisher={IEEE}
+@article{mediaeval00056,
+ author = {M Brenner and E Izquierdo},
+ title = {MediaEval Benchmark: Social Event Detection in collaborative photo collections.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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-@phdthesis{muszynski2018recognizing,
- title={Recognizing film aesthetics, spectators' affect and aesthetic emotions from multimodal signals},
- author={M Muszynski},
- year={2018},
- school={University of Geneva},
- journal={PhD Thesis}
+@article{mediaeval00057,
+ author = {X Liu and B Huet and R Troncy},
+ title = {EURECOM@ MediaEval 2011 Social Event Detection Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- journal={Big Data Analytics for Large-Scale Multimedia Search},
- pages={121},
- year={2019},
- publisher={John Wiley \& Sons}
+@article{mediaeval00058,
+ author = {LT Li and J Almeida and R da Silva Torres},
+ title = {RECOD Working Notes for Placing Task MediaEval 2011.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- journal={Big Data Analytics for Large-Scale Multimedia Search},
- pages={183},
- year={2019},
- publisher={John Wiley \& Sons}
+@article{mediaeval00005,
+ author = {J Cigarran and V Fresno and A García-Serrano and D Hernández-Aranda & R Granados},
+ title = {UNED at MediaEval 2011: can delicious help us to improve automatic video tagging?},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- author={B Bischke and D Borth and A Dengel},
- journal={Big Data Analytics for Large-Scale Multimedia Search},
- pages={157},
- year={2019},
- publisher={John Wiley \& Sons}
+@article{mediaeval00059,
+ author = {Y Wang and L Xie and H Sundaram},
+ title = {Social Event Detection with clustering and filtering.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- title={Neural Approaches Towards Computational Journalism},
- author={D Khattar},
- year={2019},
- school={International Institute of Information Technology Hyderabad},
- journal={PhD Thesis}
+@article{mediaeval00060,
+ author = {C Hauff and GJ Houben},
+ title = {WISTUD at MediaEval 2011: Placing Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- author={MC Madhavi and HA Patil},
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- volume={58},
- pages={175--202},
- year={2019},
- publisher={Elsevier}
+@article{mediaeval00061,
+ author = {S Papadopoulos and C Zigkolis and Y Kompatsiaris and A Vakali},
+ title = {CERTH@ MediaEval 2011 Social Event Detection Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- year={2019}
+@article{mediaeval00062,
+ author = {I Söke and J Tejedor and M Fapso and J Colás},
+ title = {BUT-HCTLab approaches for Spoken Web Search-MediaEval 2011.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
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- booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- pages={2502--2506},
- year={2019},
- organization={IEEE}
+@article{mediaeval00063,
+ author = {G Gninkoun and M Soleymani},
+ title = {Automatic violence scenes detection: A multi-modal approach},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
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- booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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+ title = {Leveraging linked data in Social Event Detection.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
-
-@article{ibrahim2019large,
- title={Large-scale Text-based Video Classification using Contextual Features},
- author={ZAA Ibrahim and S Haidar and I Sbeity},
- journal={European Journal of Electrical Engineering and Computer Science},
- volume={3},
- number={2},
- year={2019}
+@article{mediaeval00065,
+ author = {X Anguera},
+ title = {Telefonica System for the Spoken Web Search Task at Mediaeval 2011.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011}
}
-
-@article{lago2019visual,
- title={Visual and Textual Analysis for Image Trustworthiness Assessment within Online News},
- author={F Lago and QT Phan and G Boato},
- journal={Security and Communication Networks},
- volume={2019},
- year={2019},
- publisher={Hindawi}
+@article{mediaeval00066,
+ author = {E Barnard and M Davel and C van Heerden and N Kleynhans and K Bali},
+ title = {Phone recognition for spoken web search},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{peixoto2019toward,
- title={Toward Subjective Violence Detection in Videos},
- author={B Peixoto and B Lavi and JPP Martin and S Avila and Z Dias and A Rocha},
- booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
- pages={8276--8280},
- year={2019},
- organization={IEEE}
+@article{mediaeval00067,
+ author = {C Penet and CH Demarty and G Gravier and P Gros},
+ title = {Technicolor and inria/irisa at mediaeval 2011: learning temporal modality integration with bayesian networks},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
+}
+@article{mediaeval00068,
+ author = {A Muscariello and G Gravier},
+ title = {Irisa MediaEval 2011 Spoken Web Search System.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{wang2019video,
- title={Video Affective Content Analysis by Exploring Domain Knowledge},
- author={S Wang and C Wang and T Chen and Y Wang and Y Shu and Q Ji},
- journal={IEEE Transactions on Affective Computing},
- year={2019},
- publisher={IEEE}
+@article{mediaeval00069,
+ author = {E Acar and S Spiegel and S Albayrak and DAI Labor},
+ title = {MediaEval 2011 Affect Task: Violent Scene Detection combining audio and visual Features with SVM.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{khattar2019mvae,
- title={MVAE: Multimodal Variational Autoencoder for Fake News Detection},
- author={Khattar, Dhruv and Goud, Jaipal Singh and Gupta, Manish and Varma, Vasudeva},
- booktitle={The World Wide Web Conference},
- pages={2915--2921},
- year={2019},
- organization={ACM}
+@article{mediaeval00070,
+ author = {F Krippner and G Meier and J Hartmann and R Knauf},
+ title = {Placing media items using the Xtrieval Framework.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{yuan2019diversified,
- title={Diversified textual features based image retrieval},
- author={Yuan, Bo and Gao, Xinbo},
- journal={Neurocomputing},
- volume={357},
- pages={116--124},
- year={2019},
- publisher={Elsevier}
+@article{mediaeval00071,
+ author = {M Morchid and G Linarès},
+ title = {Mediaeval benchmark: Social Event Detection using LDA and external resources.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{benavent2019fca,
- title={FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images},
- author={Benavent, Xaro and Castellanos, Angel and de Ves, Esther and Garc{\'\i}a-Serrano, Ana and Cigarr{\'a}n, Juan},
- journal={Future Generation Computer Systems},
- volume={100},
- pages={250--265},
- year={2019},
- publisher={Elsevier}
+@article{mediaeval00072,
+ author = {H Glotin and J Razik and S Paris and JM Prevot},
+ title = {Real-time entropic unsupervised violent scenes detection in Hollywood movies-DYNI@ MediaEval Affect Task 2011.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{lux2019summarizing,
- title={Summarizing E-Sports Matches and Tournaments},
- author={Lux, Mathias and Halvorsen, P{\aa}l and Dang-Nguyen, Duc-Tien and Stensland, H{\aa}kon and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael},
- booktitle={Workshop on Immersive Mixed and Virtual Environment Systems, Amherst, MA, USA},
- year={2019}
+@article{mediaeval00073,
+ author = {D Ferrés and H Rodríguez},
+ title = {TALP at MediaEval 2011 Placing Task: Georeferencing Flickr videos with geographical knowledge and information retrieval.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{dong2019bidirectional,
- title={Bidirectional Convolutional Recurrent Sparse Network (BCRSN): An Efficient Model for Music Emotion Recognition},
- author={Dong, Yizhuo and Yang, Xinyu and Zhao, Xi and Li, Juan},
- journal={IEEE Transactions on Multimedia},
- year={2019},
- publisher={IEEE}
+@article{mediaeval00074,
+ author = {B Safadi and G Quénot},
+ title = {Lig at mediaeval 2011 affect task: use of a generic method},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{potthast2019tira,
- title={TIRA Integrated Research Architecture},
- author={Potthast, Martin and Gollub, Tim and Wiegmann, Matti and Stein, Benno},
- journal={Information Retrieval Evaluation in a Changing World-Lessons Learned from},
- volume={20},
- year={2019}
+@article{mediaeval00075,
+ author = {M Rouvier and G Linarès},
+ title = {LIA@ MediaEval 2011: Compact representation of heterogeneous descriptors for video genre classification.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{dourado2019event,
- title={Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models},
- author={Dourado, Icaro Cavalcante and Tabbone, Salvatore and da Silva Torres, Ricardo},
- booktitle={International Workshop on Graph-Based Representations in Pattern Recognition},
- pages={88--98},
- year={2019},
- organization={Springer}
+@article{mediaeval00076,
+ author = {S Rudinac and M Larson and A Hanjalic},
+ title = {TUD-MIR at MediaEval 2011 Genre Tagging Task: Query expansion from a limited number of labeled videos.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{orjesek2019dnn,
- title={DNN Based Music Emotion Recognition from Raw Audio Signal},
- author={Orjesek, Richard and Jarina, Roman and Chmulik, Michal and Kuba, Michal},
- booktitle={2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA)},
- pages={1--4},
- year={2019},
- organization={IEEE}
+@article{mediaeval00077,
+ author = {S Schmiedeke and P Kelm and T Sikora},
+ title = {TUB@ MediaEval 2011 genre tagging task: prediction using bag-of-(visual)-words approaches},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{wu2019does,
- title={Does Diversity Affect User Satisfaction in Image Search},
- author={Wu, Zhijing and Zhou, Ke and Liu, Yiqun and Zhang, Min and Ma, Shaoping},
- journal={ACM Transactions on Information Systems (TOIS)},
- volume={37},
- number={3},
- pages={35},
- year={2019},
- publisher={ACM}
+@article{mediaeval00078,
+ author = {K Schmidt and T Korner and S Heinich and T Wilhelm},
+ title = {A Two-step Approach to Video Retrieval based on ASR transcriptions.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{saravi2019use,
- title={Use of artificial intelligence to improve resilience and preparedness against adverse flood events},
- author={Saravi, Sara and Kalawsky, Roy and Joannou, Demetrios and Rivas-Casado, Monica and Fu, Guangtao and Meng, Fanlin},
- journal={Water},
- volume={11},
- number={5},
- pages={973},
- year={2019},
- publisher={Multidisciplinary Digital Publishing Institute}
+@article{mediaeval00079,
+ author = {R Aly and T Verschoor and R Ordelman},
+ title = {UTwente does Rich Speech Retrieval at MediaEval 2011.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{dourado2019event,
- title={Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models},
- author={Dourado, Icaro Cavalcante and Tabbone, Salvatore and da Silva Torres, Ricardo},
- booktitle={International Workshop on Graph-Based Representations in Pattern Recognition},
- pages={88--98},
- year={2019},
- organization={Springer}
+@article{mediaeval00080,
+ author = {C Wartena and M Larson},
+ title = {Rich Speech Retrieval Using Query Word Filter.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{lux2019summarizing,
- title={Summarizing E-sports matches and tournaments: the example of counter-strike: global offensive},
- author={Lux, Mathias and Halvorsen, P{\aa}l and Dang-Nguyen, Duc-Tien and Stensland, H{\aa}kon and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael},
- booktitle={Proceedings of the 11th ACM Workshop on Immersive Mixed and Virtual Environment Systems},
- pages={13--18},
- year={2019},
- organization={ACM}
+@article{mediaeval00081,
+ author = {M Ruocco and H Ramampiaro},
+ title = {Ntnu@ mediaeval2011: Social event detection task (sed)},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@phdthesis{ammar2019using,
- title={Using deep learning algorithms to detect violent activities},
- author={Ammar, SM and Anjum, Md and Rounak, Tanvir and Islam, Md and Islam, Touhidul and others},
- year={2019},
- school={BRAC University},
- journal={PhD Thesis}
+@article{mediaeval00082,
+ author = {T Semela and HK Ekenel},
+ title = {KIT at MediaEval 2011-Content-based genre classification on web-videos.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{ram2019multilingual,
- title={Multilingual Bottleneck Features for Query by Example Spoken Term Detection},
- author={Ram, Dhananjay and Miculicich, Lesly and Bourlard, Herv{\'e}},
- journal={arXiv preprint arXiv:1907.00443},
- year={2019}
+@article{mediaeval00083,
+ author = {R Tiwari and C Zhang and M Montes},
+ title = {UAB at MediaEval 2011: Genre Tagging Task.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{abebe2019generic,
- title={Generic metadata representation framework for social-based event detection, description, and linkage},
- author={Abebe, Minale A and Tekli, Joe and Getahun, Fekade and Chbeir, Richard and Tekli, Gilbert},
- journal={Knowledge-Based Systems},
- year={2019},
- publisher={Elsevier}
+@article{mediaeval00084,
+ author = {JM Perea-Ortega and A Montejo-Ráez and MC Díaz-Galiano and MT Martín-Valdivia},
+ title = {Genre tagging of videos based on information retrieval and semantic similarity using WordNet.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{cogan2019mapgi,
- title={MAPGI: Accurate identification of anatomical landmarks and diseased tissue in gastrointestinal tract using deep learning},
- author={Cogan, Timothy and Cogan, Maribeth and Tamil, Lakshman},
- journal={Computers in biology and medicine},
- volume={111},
- pages={103351},
- year={2019},
- publisher={Elsevier}
+@article{mediaeval00085,
+ author = {W Alink and R Cornacchia},
+ title = {Out-of-the-box strategy for Rich Speech Retrieval MediaEval 2011.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@article{tejedor2019search,
- title={Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation},
- author={Tejedor, Javier and Toledano, Doroteo T and Lopez-Otero, Paula and Docio-Fernandez, Laura and Pe{\~n}agarikano, Mikel and Rodriguez-Fuentes, Luis Javier and Moreno-Sandoval, Antonio},
- journal={EURASIP Journal on Audio, Speech, and Music Processing},
- volume={2019},
- number={1},
- pages={13},
- year={2019},
- publisher={SpringerOpen}
+@article{mediaeval00086,
+ author = {M Eskevich and GJF Jones},
+ title = {DCU at MediaEval 2011: Rich Speech Retrieval (RSR)},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@inproceedings{li2019affective,
- title={Affective Video Content Analyses by Using Cross-Modal Embedding Learning Features},
- author={Li, Benchao and Chen, Zhenzhong and Li, Shan and Zheng, Wei-Shi},
- booktitle={2019 IEEE International Conference on Multimedia and Expo (ICME)},
- pages={844--849},
- year={2019},
- organization={IEEE}
+@article{mediaeval00087,
+ author = {B Ionescu and K Seyerlehner and C Vertan and P Lambert},
+ title = {Audio-Visual content description for video genre classification in the context of social media.},
+ journal = {MediaEval Working Notes Proceedings},
+ year = {2011},
}
-
-@phdthesis{kirkerod2019unsupervised,
- title={Unsupervised preprocessing of medical imaging data with generative adversarial networks},
- author={Kirker{\o}d, Mathias},
- year={2019},
- journal={Master Thesis}
-}
\ No newline at end of file
diff --git a/_config.yml b/_config.yml
index 2fdce20cf..cea0de8f9 100644
--- a/_config.yml
+++ b/_config.yml
@@ -1,19 +1,20 @@
# SITE CONFIGURATION
-baseurl: "/"
-url: ""
+base_url: "https://multimediaeval.github.io"
+url: "https://multimediaeval.github.io"
title: "MediaEval Benchmark" # site's title
description: "MediaEval Benchmarking Initiative Multimedia Evaluation" # used by search engines
+
defaults:
-
scope:
path: "" # an empty string here means all files in the project
values:
- image: assets/img/mediaeval-white.png # seo tag
+ image: "/assets/img/twitter-card.png" # seo tag
# THEME-SPECIFIC CONFIGURATION
theme_settings:
# Meta
- avatar: assets/img/mediaeval-white.png
+ avatar: assets/img/mediaeval-favicon.png
color_image: /assets/img/lineart.png # In post when you set color
favicon: assets/img/mediaeval-favicon.png
@@ -42,7 +43,7 @@ theme_settings:
wordpress:
youtube:
- # Sharing options
+ # Sharing options none
share_buttons:
facebook:
twitter:
@@ -85,6 +86,8 @@ paginate_path: "blog/page:num"
# BUILD SETTINGS
markdown: kramdown
+kramdown:
+ input: GFM
highlighter: rouge
sass:
sass_dir: _sass
@@ -92,7 +95,7 @@ sass:
plugins:
- jekyll-paginate
- jekyll-seo-tag
- - jekyll-scholar
+ - jekyll/scholar
exclude: [
".jekyll-cache",
"Gemfile",
@@ -102,11 +105,7 @@ exclude: [
".idea",
"LICENSE",
"README.md",
- "screenshot.png",
- "assets/package.json",
- "assets/node_modules",
- "assets/gulpfile.js"
- ]
+ ]
# theme: type-on-strap # Uncomment if using the theme as a jekyll theme gem
# remote_theme: sylhare/Type-on-Strap # If using as a remote_theme in github
@@ -115,3 +114,9 @@ collections:
editions:
output: true
permalink: /:collection/:path/
+
+# BIBLIOGRAPHY SETTINGS
+scholar:
+ bibliography_list_tag: ul
+ sort_by: year
+ order: descending
diff --git a/_editions/2020.md b/_editions/2020.md
index 7b6828905..8216f2151 100644
--- a/_editions/2020.md
+++ b/_editions/2020.md
@@ -5,4 +5,76 @@ year: 2020
permalink: /editions/2020/
---
-More information coming soon.
+The MediaEval Multimedia Evaluation benchmark offers tasks that are related to multimedia retrieval, analysis, and exploration. Participation is open to interested researchers who register. MediaEval focuses specifically on the human and social aspects of multimedia, and on multimedia systems that serve users. They offer the opportunity for researchers to tackle challenges that bring together multiple modalities (visual, text, music, sensor data).
+
+
+
+### Workshop
+The MediaEval 2020 Workshop took place 14-15 December 2020 fully online.
+* Workshop program and acknowledgements: [MediaEval 2020 Workshop Program](https://multimediaeval.github.io/editions/2020/docs/MediaEval2020WorkshopScheduleAndThanks.pdf)
+* Workshop proceedings: [MediaEval 2020 Working Notes Proceedings](http://ceur-ws.org/Vol-2882)
+* Presentation slides can be found on the [MediaEval Slideshare](https://www.slideshare.net/multimediaeval/presentations)
+* Videos of presentations can be on the [MediaEval YouTube Channel](https://www.youtube.com/channel/UCc-1NW1Uo2o_zI4F81iyTcw/videos)
+
+Workshop group photo:
+
+
+
+
+
+
+
+### Schedule
+* End of July 2020: First data releases
+* Mid November 2020: Runs due (See individual task pages for the exact deadlines)
+* 30 November 2020: Working notes paper
+* 11,14-15 December 2020: MediaEval 2020 Workshop (Fully online.)
+
+#### The MediaEval Organization
+MediaEval is made possible by the efforts of a larger number of task organizers, who each are responsible for organizing their own tasks. Please see the individual task pages for their name. The over all coordination is carried out by the MediaEval Logistics Committe and guided by the Community Council.
+
+##### The MediaEval Logistics Committee (2020)
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Steven Hicks, SimulaMet, Norway
+* Ngoc-Thanh Nguyen, University of Information Technology, Vietnam
+* Ricardo Manhães Savii, Dafiti Group, Brasil
+* With special thanks to Bart Thomee, Verily, US
+
+##### The MediaEval Community Council (2020)
+* Martha Larson, Radboud University, Netherlands (Coordinator and contact person)
+* Gareth J. F. Jones, Dublin City University, Dublin, Ireland
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+
+
+
+MediaEval is grateful for the support of [ACM Special Interest Group on Multimedia](http://sigmm.org/)
+
+
+
+For more information, contact m.larson (at) ru.cs.nl. You can also follow us on Twitter @multimediaeval
diff --git a/_editions/2020/docs/MediaEval2020GroupPhoto.png b/_editions/2020/docs/MediaEval2020GroupPhoto.png
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index 000000000..5c40e2315
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diff --git a/_editions/2020/docs/MediaEval2020WorkshopScheduleAndThanks.pdf b/_editions/2020/docs/MediaEval2020WorkshopScheduleAndThanks.pdf
new file mode 100644
index 000000000..3a9c8de7b
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diff --git a/_editions/2020/docs/MediaEval2020_UsageAgreement.pdf b/_editions/2020/docs/MediaEval2020_UsageAgreement.pdf
new file mode 100644
index 000000000..92fac4628
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diff --git a/_editions/2020/docs/MediaEval2021GroupPhoto.png b/_editions/2020/docs/MediaEval2021GroupPhoto.png
new file mode 100644
index 000000000..ba26e3cd2
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diff --git a/_editions/2020/docs/README.md b/_editions/2020/docs/README.md
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/_editions/2020/docs/README.md
@@ -0,0 +1 @@
+
diff --git a/_editions/2020/docs/sigmmlogo.gif b/_editions/2020/docs/sigmmlogo.gif
new file mode 100644
index 000000000..58d8d559f
Binary files /dev/null and b/_editions/2020/docs/sigmmlogo.gif differ
diff --git a/_editions/2020/tasks/README.md b/_editions/2020/tasks/README.md
new file mode 100644
index 000000000..6f4f0cb7b
--- /dev/null
+++ b/_editions/2020/tasks/README.md
@@ -0,0 +1,31 @@
+This folder contains `Markdown` (.md) files to all tasks for 2020 MediaEval edition.
+
+## How to edit
+
+Opening a file and clicking on the pencil logo (view Figure 1 below)
+
+
+
+you will access `edit` mode on the file aand you will see something like below:
+
+
+
+There are 2 main parts to the document:
+
+* part 1 (lines 1 to 11) is the task file metadata. Here, you (task organizer), should fill in all `# required info` fields (title, subtitle, and blurb). When your task content is ready to be published on the website, to be shown on the website, then you should edit the `hide` property to `false`, this way your task will be visible on the website.
+
+* part 2 (lines 12 to infinity) is the actual task content. There is a suggested structure to the document to be followed. This part accepts content with [Markdown](https://daringfireball.net/projects/markdown/syntax) and HTML syntax.
+
+After you fill all content `commit changes` by filling the form below that edit screen and clicking on `Propose changes` as shown in Figure 3 below:
+
+
+
+That action will open a new window in which you will confirm a `Pull request`. As you can see in Figure 4 below:
+* yellow arrow points out where you can select a reviewer (if you are already talking to one of the website admins), this is optional
+* fill your comments on the `fill here` space as you believe it's required to support approval of your change.
+* red arrow points to the button that confirms your `Pull request`
+
+
+
+Other than that please feel free to ask for help. This structure is and experiment and we need help to turn it useful and easy to everyone. MediaEval organizers are available to help or submit questions and issues [here](https://github.com/multimediaeval/multimediaeval.github.io/issues).
+
diff --git a/_editions/2020/tasks/fakenews.md b/_editions/2020/tasks/fakenews.md
index a190d3d9f..a62a30b35 100644
--- a/_editions/2020/tasks/fakenews.md
+++ b/_editions/2020/tasks/fakenews.md
@@ -2,61 +2,85 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title: FakeNews Task
-subtitle: subtitle
-blurb: This is the blurb for fakenews task
+title: "FakeNews: Corona virus and 5G conspiracy"
+subtitle: Corona virus and 5G conspiracy
+blurb: "Spontaneous and intentional digital Fake News wildfires over on-line social media can be as dangerous as natural fires. A new generation of data mining and analysis algorithms is required for early detection and tracking of information waves. This task focuses on the analysis of tweets around Coronavirus and 5G conspiracy theories in order to detect misinformation spreaders."
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+The Fake News Detection Task 2020 offers two Fake News Detection subtasks on COVID-19 and 5G conspiracy topics. The first subtask includes NLP-based Fake News Detection while the second subtask targets the detection of abnormal spreading patterns. Both tasks are related to misinformation disseminated in the context of the COVID-19 crisis. More specifically, misinformation claims that the construction of the 5G network and the associated electromagnetic radiation trigger the SARS-CoV-2 virus.
-#### Introduction
+***NLP-Based Fake News Detection***: In this subtask, the participants receive a dataset, including 10.000 Tweets in English related to COVID-19 and 5G and their corresponding metadata. The participants are encouraged to build a binary classifier that can predict whether a Tweet contains the disinformation described above or whether it only accidentally contains the two buzzwords.
+***Structure-Based Fake News Detection***: Here, the participants receive a data record with German tweets; some of these tweets are also related to the false information described above. Although the content of the tweets is automatically translated into English, it is only secondary because each tweet contains a distribution graph in addition to the text. A distribution graph here is the subgraph of the Twitter follower network, which contains the nodes (Twitter users) who shared the respective tweet.
-#### New for 2020
+#### Motivation and Background
+Digital wildfires, i.e., fast-spreading inaccurate, counterfactual, or intentionally misleading information, can quickly permeate public consciousness and have severe real-world implications and are among the top global risks in the 21st century. While a sheer endless amount of misinformation exists on the internet, only a small fraction of it spreads far and affects people to a degree where they commit harmful and/or criminal acts in the real world. The COVID-19 pandemic has severely affected people worldwide, and consequently, it has dominated world news for months. Thus, it is no surprise that it has also been the topic of a massive amount of misinformation, which was most likely amplified by the fact that many details about the virus were unknown at the start of the pandemic. We focus on specific misinformation - *the idea that the COVID-19 outbreak is somehow connected to the introduction of the 5G wireless technology*.
-#### Target group
+#### Target Group
+The task is of interest to researchers in the areas of online news, social media, multimedia analysis, multimedia information retrieval, natural language processing, and meaning understanding and situational awareness to participate in the challenge.
#### Data
-
-
-#### Ground Truth
+The dataset contains two sets of tweets mentioning Corona Virus and 5G that include text, reposting time patterns, and some basic info about reposters. The first set consists of only English language posts. The second set consists of translated tweets from German Twitter accounts only. The datasets are balanced with respect to the number of samples of different classes and variations of the amount of data that accompanies each tweet. We provide a wide variety of short, medium, and long tweets with neutral, positive, negative, and sarcastic phrasing.
#### Evaluation Methodology
+The ground truth is created by manual annotation of the collected tweets based on text-only analysis by a group of experts and volunteers. The main metric will be ROC AUC, but the task may explore other metrics as well.
#### References and recommended reading
+***General***
+
+[1] Nyhan, Brendan, and Jason Reifler. 2015. [Displacing misinformation about events: An experimental test of causal corrections](https://www.cambridge.org/core/journals/journal-of-experimental-political-science/article/displacing-misinformation-about-events-an-experimental-test-of-causal-corrections/69550AB61F4E3F7C2CD03532FC740D05#). Journal of Experimental Political Science 2, no. 1, 81-93.
+
+***Twitter data collection and analysis***
-### Big Picture of the Task
+[2] Schroeder, Daniel Thilo, Konstantin Pogorelov, and Johannes Langguth. 2019. [FACT: a Framework for Analysis and Capture of Twitter Graphs](https://ieeexplore.ieee.org/document/8931870). In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 134-141.
-#### Innovation
+[3] Achrekar, Harshavardhan, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, and Benyuan Liu. 2011. [Predicting flu trends using twitter data](https://ieeexplore.ieee.org/document/5928903). In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, 702-707.
+[4] Chen, Emily, Kristina Lerman, and Emilio Ferrara. 2020. [Covid-19: The first public coronavirus twitter dataset](https://arxiv.org/abs/2003.07372v1?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+CoronavirusArXiv+%28Coronavirus+Research+at+ArXiv%29). arXiv preprint arXiv:2003.07372.
-#### Focus
+[5] Kouzy, Ramez, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. [Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152572/). Cureus 12, no. 3.
+***Natural language processing***
-#### Risk management
+[6] Bourgonje, Peter, Julian Moreno Schneider, and Georg Rehm. 2017. [From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles](https://www.aclweb.org/anthology/W17-4215/). In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, 84-89.
+[7] Imran, Muhammad, Prasenjit Mitra, and Carlos Castillo. 2016. [Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages](https://arxiv.org/abs/1605.05894). arXiv preprint arXiv:1605.05894.
-#### Task organization team
+***Information spreading***
+[8] Liu, Chuang, Xiu-Xiu Zhan, Zi-Ke Zhang, Gui-Quan Sun, and Pak Ming Hui. 2015. [How events determine spreading patterns: information transmission via internal and external influences on social networks](https://iopscience.iop.org/article/10.1088/1367-2630/17/11/113045/pdf). New Journal of Physics 17, no. 11.
-#### Task organizers
+
+
-#### Task auxiliaries
+
+#### Task Organizers
+* Konstantin Pogorelov, Simula Research laboratory (Simula), Norway, konstantin (at) simula.no
+* Johannes Langguth, Simula Research laboratory (Simula), Norway, langguth (at) simula.no
+* Daniel Thilo Schroeder, Simula Research laboratory (Simula), Norway
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 10 August: Data release
+* ~~31 October~~ 16 November: Runs due + Start working on Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December : MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
+
diff --git a/_editions/2020/tasks/floodmultimedia.md b/_editions/2020/tasks/floodmultimedia.md
new file mode 100644
index 000000000..06b00059e
--- /dev/null
+++ b/_editions/2020/tasks/floodmultimedia.md
@@ -0,0 +1,91 @@
+---
+# static info
+layout: task
+year: 2020
+hide: false
+
+# required info
+title: Flood-related Multimedia
+subtitle: Flood classification in social multimedia for Northeastern Italy
+blurb: Floods are one of the most common natural disasters that occur on our planet, and the destruction they cause is enormous. In this task, the participants receive a set of Twitter posts (tweets), including text, images, and other metadata, and are asked to automatically identify which posts are truly relevant to flooding incidents in the specific area of Northeastern Italy. The ground truth labels have been created by experts in flood risk management. The ultimate aim of this task is to develop technology that will support experts in flood disaster management.
+---
+
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
+
+#### Task Description
+The Flood-related Multimedia Task tackles the analysis of social multimedia from Twitter for flooding events. In this task, the participants receive a set of Twitter posts (tweets) and their associated images, which contain keywords related to floods in a specific area of interest, specifically, the Eastern Alps partition in Northeastern (NE) Italy. However, the relevance of the tweets to actual flooding incidents in that area is ambiguous. The objective of the task is to build an information retrieval system or a classifier that is able to distinguish whether or not a tweet is relevant to a flooding event in the examined area.
+
+The dataset of the task consists of Italian-language tweets, motivated by the common flood events in the cities of Eastern Alps (e.g., Venice, Vicenza, Trieste) and surrounding areas. Participants can tackle the task using text features, image features, or a combination of both. We choose Italian for this task in order to encourage researchers to move away from a focus on English.
+
+
+
+*The area of interest: Eastern Alps partition in North-East Italy*
+
+#### Motivation and Background
+[Floods](https://www.nationalgeographic.com/environment/natural-disasters/floods/) are a natural disaster that affects most places on Earth and causes a vast number of deaths and damages. Citizen crowdsourcing and social media posts have been proven valuable in all stages of managing such a disaster: a) they can notify about a possible disaster in the pre-emergency phase; b) they can provide insights on the evolution of the incident and detect areas in danger during the disaster; and c) they can assist in the damage control in the post-emergency phase.
+
+However, the large and continuous streams of published posts carry a lot of noise (e.g., the metaphorical use of flood-related words, incidents outside the area of interest, past events), which makes it difficult to collect high-quality information. Automatic estimation of a tweet’s relevance could address this challenge, by filtering out unrelated posts. Better ability to separate relevant and not relevant tweets will contribute to improving the quality of the incoming information available to support first responders and civil protection authorities.
+
+#### Target Group
+Researchers in the areas of social media, multimedia and multilingual analysis, multimedia classification and information retrieval are strongly encouraged to participate in the challenge. Industries and SMEs that develop similar AI technologies for semantic data fusion and retrieval of multi- or cross-lingual content are also warmly invited to participate. In addition, the task could be of interest to researchers and practitioners in the domains of disaster management, emergency response, situational awareness, water management, and any other flood-related domains.
+
+#### Data
+The dataset is a list of social media posts that have been collected from Twitter between 2017 and 2019, by searching for Italian flood-related keywords (e.g., “allagamento”, “alluvione”) inside the tweet text. All tweets contain an attached image and should be still online at the time of releasing the dataset. In order to be compliant with the Twitter Developer Policy, only the IDs of the tweets can be distributed, but a tool to download them will also be provided.
+
+The ground truth of the dataset has been collected with human annotation, by the Alto Adriatico Water Authority (AAWA), who are experts on flood risk management in the Eastern Alps partition of NE Italy. It should be noted here that only tweets that refer to floods in this specific region have been annotated as relevant.
+
+#### Evaluation Methodology
+The evaluation metric for the binary classification of tweets as relevant (1) or not relevant (0) will be F-score.
+
+#### References and recommended reading
+
+
+[1] Moumtzidou, A., Andreadis, S., Gialampoukidis, I., Karakostas, A., Vrochidis, S. and Kompatsiaris, I., 2018, April. [Flood relevance estimation from visual and textual content in social media streams](https://dl.acm.org/doi/abs/10.1145/3184558.3191620). In Companion Proceedings of the The Web Conference 2018 (pp. 1621-1627).
+
+[2] Peters, R. and de Albuquerque, J.P., 2015. [Investigating images as indicators for relevant social media messages in disaster management](). In ISCRAM.
+
+[3] Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C., Squicciarini, A.C. and Tapia, A.H., 2015, May. [Twitter Mining for Disaster Response: A Domain Adaptation Approach](http://www.agora.icmc.usp.br/site/wp-content/uploads/2015/08/Peters-and-Albuquerque-2015-Investigating-images-as-indicators-for-relevant-social-media-messages-in-disaster-management.pdf). In ISCRAM.
+
+[4] Imran, M., Castillo, C., Lucas, J., Meier, P. and Vieweg, S., 2014, April. [AIDR: Artificial intelligence for disaster response](https://dl.acm.org/doi/abs/10.1145/2567948.2577034). In Proceedings of the 23rd International Conference on World Wide Web (pp. 159-162).
+
+[5] Brouwer, T., Eilander, D., Van Loenen, A., Booij, M.J., Wijnberg, K.M., Verkade, J.S. and Wagemaker, J., 2017. [Probabilistic flood extent estimates from social media flood observations](https://core.ac.uk/download/pdf/207400745.pdf). Natural Hazards & Earth System Sciences, 17(5).
+
+
+We also recommend to read past years’ task papers in the MediaEval Proceedings:
+
+[1] Proceedings of the MediaEval 2019 Workshop.
+
+[2] Martha Larson, et al. (eds.) 2018. [Proceedings of the MediaEval 2018 Workshop](http://ceur-ws.org/Vol-2283/), Sophia Antipolis, France, Oct. 29-31, 2018.
+
+[3] Guillaume Gravier et al. (eds.) 2017. [Proceedings of the MediaEval 2017 Workshop](http://ceur-ws.org/Vol-1984), Dublin, Ireland, Sept. 13-15, 2017.
+
+
+#### Task Organizers
+
+Stelios Andreadis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece, andreadisst@iti.gr
+Ilias Gialampoukidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Anastasios Karakostas, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Stefanos Vrochidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Ioannis Kompatsiaris, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Roberto Fiorin, Alto Adriatico Water Authority (AAWA), Italy
+Daniele Norbiato, Alto Adriatico Water Authority (AAWA), Italy
+Michele Ferri, Alto Adriatico Water Authority (AAWA), Italy
+
+#### Task Auxiliaries
+
+Benjamin Bischke, German Research Center for Artificial Intelligence (DFKI), Germany
+Patrick Helber, German Research Center for Artificial Intelligence (DFKI), Germany
+
+#### Task Schedule
+* 31 July: Data release
+* ~~23 October~~ 9 November: Runs due
+* 16 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop (Fully virtual)
+
+Workshop will be held online.
+
+
+
+**Acknowledgment:** This task has been supported by the EU’s Horizon 2020 research and innovation programme under grant agreements H2020-700475 [beAWARE](https://beaware-project.eu/), H2020-776019 [EOPEN](https://eopen-project.eu/) and H2020-832876 [aqua3S](https://aqua3s.eu/).
diff --git a/_editions/2020/tasks/gamestory.md b/_editions/2020/tasks/gamestory.md
index db1b048c6..485ec3efc 100644
--- a/_editions/2020/tasks/gamestory.md
+++ b/_editions/2020/tasks/gamestory.md
@@ -2,61 +2,79 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "Emotional Mario: Believable AI agents in video games"
+subtitle:
+blurb: The Emotional Mario task explores the use of human emotion to improve the performance of AI-based agents playing Super Mario Bros. We provide a multimodal dataset consisting of video and sensor data to be used to complete two different subtasks.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+Emotional Mario is structured into two subtasks:
+
+1. In the first subtask, *Artificial Skill*, participants produce an agent that is able to play Super Mario Bros as well as possible. The goal of this task is for the agent (Mario) to get as far as possible in the game (number of levels beat) with the least amount of lives lost.
+2. In the second subtask, *Human or Not*, participants of the Emotional Mario task are expected to create an AI that controls Mario throughout the game. We ask participants to submit AIs for two levels of skill: (i) skilled player and (ii) novice player. A jury will then be presented with videos from human players and AIs in a random sequence and has to judge whether the playthrough in the video originates from a skilled or novice player and if they think a human or an AI controls the game, just like in a Turing test.
-#### Introduction
+Minimizing time and maximizing score and progress has been topic to the Mario AI competition [3]. In the Emotional Mario task, we go beyond these goals and create a more humanlike AI agent that is informed by human emotion. The task provides a data set of videos and sensor readings of people playing Super Mario Bros. Participants use this data set as a source of information about human players' emotional reactions.
+#### Motivation and Background
+With the rise of deep learning, many large leaps in AI research have been achieved in recent years, such as human-level image recognition or text classification. In tasks like image classification, autonomous driving, or personal assistants, researchers have been able to create believable AI agents that behave like human beings. However, in certain areas, the behavior of AI agents remains far from humanlike. This gap is strikiingly evident with AI agents that are created to play repetitive video games. Human players readily recognize the machine nature of such agents. In the Emotional Mario task, we attempt to address this gap by asking researchers to create more believable AI agents that play one of the most iconic classic video games: Super Mario Bros.
-#### New for 2020
-
-
-#### Target group
+Artificial intelligence in commercial video games is only rarely genuinely intelligent. The behavior of bots (seemingly autonomous players) and non-player characters (NPCs) is typically scripted, rule-based, or utilizes finite-state machines. One of the reasons for that is that the goal of game AI is typically not to simulate life, but to create an experience for players that fits the expectations of the game designers [1]. However, with the advent of multiplayer games, several game genres have lost a lot of popularity. Games heavily relying on strategy, like StarCraft, League of Legends, DOTA, or Counter-Strike, are typically played against other human players, as scripted bots, and even highly sophisticated machine learning-based agents provide the quality of experience expected by players [2]. However, in the Mario AI competition, it has been noted that many of the agents entering the competition behaved non-human like [3]. We strongly believe that AI behaving more human-like is a necessary next step. Not only for interactive entertainment and media content creation, where methods for human-like AI could reduce development times drastically, but also serious and educational games, conversational AIs like personal assistants, and behavior of robots and autonomous vehicles would greatly benefit from research in that direction.
+#### Target Group
+The target group for this task is diverse and broad. It includes researchers and practitioners from game design and development, game studies, machine learning, and artificial intelligence and interactive multimedia. We also encourage interdisciplinary research involving people from the media sciences, the cultural sciences, and the humanities discussing what the expected behavior could be and how they are perceived. There are multiple angles from which one can approach this task, from classical decision trees and rule-based AI down to reinforcement learning and even generative adversarial networks. In any case, regardless of the research background, submission will help to have a basic understanding of how human-like behavior is perceived and, ideally, how to mimic it.
#### Data
-
-
-#### Ground Truth
-
+We provide a multimodal dataset, Toadstool [4], consisting of ten participants playing Super Mario Bros. The dataset contains their game input, demographics, sensor output from a medical-grade device, and videos of their faces while playing. The data is free to use for academic purposes.
#### Evaluation Methodology
+The task consists of two subtasks. Both tasks are mandatory for participation. The Artificial Skill subtask is meant to measure the skill of the AI player. The evaluation metric is defined as the number of completed levels and lost lives within 10 minutes of playtime (levels divided by lost lives).
+The second subtask, called Human or Not, will be evaluated by a jury of people with different levels of experience playing computer games (at least two, expert/non-expert). The jury will be presented with a series of videos and they must identify which videos were made with a human playing the game and which videos were made with an AI playing. The possibilities between a human and an AI player is represented with a 10-point scale. The length of the video is 5 minutes with a specific set of levels. The organizers will provide some videos themself from AI and real players.
-#### References and recommended reading
+
+**Submission**
-### Big Picture of the Task
+**Sub Task 1 (artificial skill)**: The actions for an agent playing 10 minutes super Mario in the OpenAI Gym with Settings provided by the organizers.
-#### Innovation
+**Sub Task 2 (human or not)**: 5 minutes video from an agent playing super Mario in the OpenAI Gym with the Settings provided by the organizers.
-#### Focus
-
-
-#### Risk management
-
-
-#### Task organization team
+#### References and recommended reading
+
+
+[1] Rabin, S. (2014). [Game AI pro: collected wisdom of game AI professionals](http://www.gameaipro.com/). AK Peters/CRC Press.
+[2] Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., ... & Józefowicz, R. (2019). [Dota 2 with Large Scale Deep Reinforcement Learning](https://arxiv.org/abs/1912.06680).
-#### Task organizers
+[3] J. Togelius, S. Karakovskiy and R. Baumgarten, [The 2009 Mario AI Competition](https://ieeexplore.ieee.org/document/5586133), IEEE Congress on Evolutionary Computation, Barcelona, 2010, pp. 1-8, doi:10.1109/CEC.2010.5586133.
+[4] Henrik Svoren, Vajira Thambawita, Pål Halvorsen, Petter Jakobsen, Enrique Garcia-Ceja, Farzan Majeed Noori, Hugo L. Hammer, Mathias Lux, Michael Alexander Riegler, and Steven Alexander Hicks. 2020. [Toadstool: A Dataset for Training Emotional Intelligent Machines Playing Super Mario Bros.](https://dl.acm.org/doi/abs/10.1145/3339825.3394939) In Proceedings of the 11th ACM Multimedia Systems Conference (MMSys ’20). Association for Computing Machinery, New York, NY, USA, 309–314.
-#### Task auxiliaries
+#### Task Organizers
+
+* Mathias Lux, Alpen-Adria-Universität Klagenfurt, mlux (at) itec.aau.at
+* Michael Riegler, SimulaMet, michael (at) simula.no
+* Steven Hicks, SimulaMet, steven (at) simula.no
+* Duc-Tien Dang-Nguyen, University of Bergen
+* Kristine Jorgensen, University of Bergen
+* Martin Potthast, Universität Leipzig
+* Vajira Thambawita, SimulaMet
+* Pål Halvorsen, SimulaMet
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~31 October~~ 16 November: Runs due
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
+
diff --git a/_editions/2020/tasks/lifelogging.md b/_editions/2020/tasks/lifelogging.md
index db1b048c6..b13a58ec1 100644
--- a/_editions/2020/tasks/lifelogging.md
+++ b/_editions/2020/tasks/lifelogging.md
@@ -2,61 +2,112 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "Insight for Wellbeing: Multimodal personal health lifelog data analysis"
+subtitle:
+blurb: The quality of the air that we breathe as individuals as we go about our daily lives is important for health and wellbeing, However, measuring personal air quality remains a challenge. This task investigates the prediction of personal air quality using open data or data from lifelogs. The data includes images, tags, physiological data, and sensor readings.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+Task participants create systems that derive insights from multimodal lifelog data that are important for health and wellbeing. The first dataset, namely "personal air quality data" (PAQD), includes air pollution data (PM2.5, O3, and NO2) and lifelog data (e.g., physiological data, tags, and images) collected by using sensors boxes, lifelog cameras, and smartphones along the predefined routes in a city. The second dataset, namely "global air quality data" (GAQD), includes weather and air pollution data collected over the city and provided by the government and crawled from related websites.
-#### Introduction
+Participants in this task tackle two challenging subtasks:
+1. Personal Air Quality Prediction with public/open data: Task participants predict the value of personal air pollution data (PM2.5, O3, and NO2) using only weather data (wind speed, wind direction, temperature, humidity) and air pollution data (PM2.5, O3, and NO2) from public/open data sources (e.g., stations, website). This subtask's target is to investigate whether we can use public/open data to predict personal air pollution data. The personal air pollution data can be concerned as the regional air pollution data since these data a locally collected by people who carry personal equipment. In other words, the ground truth is data collected by sensor boxes carried by people.
+2. Personal Air Quality Prediction with lifelog data: participants predict the personal Air Quality Index using images captured by people (plus GAQD). The purpose of this subtask is whether we can use only lifelog data (i.e., pictures of the surrounding environment, annotations, and comments), plus some data from open sources (e.g., weather, air pollution data) to predict the personal air pollution data.
+#### Motivation and Background
+The association between people's wellbeing and the properties of the surrounding environment is an essential area of investigation. Although these investigations have a long and rich history, they have focused on the general population. There is a surprising lack of research investigating the impact of the environment on the scale of individual people. On a personal scale, local information about air pollution (e.g., PM2.5, NO2, O3), weather (e.g., temperature, humidity), urban nature (e.g., greenness, liveliness, quietness), and personal behavior (e.g., psychophysiological data) play an essential role. It is not always possible to gather plentiful amounts of such data. As a result, a key research question remains open: Can sparse or incomplete data be used to gain insight into wellbeing? Is there a hypothesis about the associations within the data so that wellbeing can be understood using a limited amount of data? Developing hypotheses about the associations within the heterogeneous data contributes towards building good multimodal models that make it possible to understand the impact of the environment on wellbeing at the local and individual scale. Such models are necessary since not all cities are fully covered by standard air pollution and weather stations, and not all people experience the same reaction to the same environment situation. Moreover, images captured by the first-person view could give essential cues to understand that environmental situations in cases in which precise data from air pollution stations are lacking.
-#### New for 2020
+Let us imagine the following scenario. Yamamoto-san is using the Image-2-AQI app to know how harmful air pollution is by merely feeding captured images to the app. Simultaneously, at the urban air pollution center, the air pollution map is updated with Yamamoto-san's contribution (e.g., images, annotation). Satoh-san, with some clicks on his smartphone, the environmental-based risk map application can show him the excellent route from A to B with less congestion and harmful air pollution. Simultaneously, less congestion from A to B is due to fewer people coincidentally traveling on the same route. Such simple apps are parts of the human-environment sustainable and co-existing system that have changed people's pro-environmental behaviors.
+The critical research question here is, "does the personal air quality be predicted by using other data that is easy to obtain?"
-#### Target group
+#### Target Group
+This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science.
#### Data
+The personal air quality data (PAQD) were collected from March to April 2019 along the marathon course of the Tokyo 2020 Olympics and the running course around the Imperial Palace using wearable sensors. There were five data collection participants assigned to five routes to collect the data. Routes 1–4 were along the marathon course for the Tokyo 2020 Olympics. Route 5 was the running course around the Imperial Palace. The length of each route was approximately 5 km. Each participant started data collection at 9 am every weekday, and it took approximately one hour to walk each route. Collected data contain weather data (e.g., temperature, humidity), atmospheric data (e.g., O3, PM2.5, and NO2), GPS data, and lifelog data (e.g., images, annotation).
+The glocal air pollution data (GAPD) contains the atmospheric monitoring station data collected by the Atmospheric Environmental Regional Observation System (AEROS) in Japan (http://soramame.taiki.go.jp). AEROS contains real-time atmospheric data at every hour for 2032 meteorological monitoring stations across Japan. The atmospheric data includes eleven types of air pollutant data (SO2, NOx, NO, NO2, CO, Ox, NMHC, CH4, THC, SPM, and PM2.5), and four types of meteorological data (wind direction, wind speed, temperature, and humidity).
-#### Ground Truth
-
+All data are stored in CSV format, except images in JPG format. Personal data are privacy protected. All task participants should sign the agreement of using these data, released by MediaEval and NICT-Japan, for research purposes only.
#### Evaluation Methodology
+The ground truth for the dataset of the two subtasks is collected as follows:
+- For the Personal Air Quality Prediction with public/open data subtask: some parts of personal (PM2.5, O3, and NO2) data are deleted and saved as the ground truth.
+- For the Personal Air Quality Prediction with lifelog data subtask: the set of personal AQI are hidden and saved as the ground truth
+
+For each subtask, the evaluation method is applied as follows:
+- For the Personal Air Quality Prediction with public/open data subtask: We use the SMAPE/RMSE/MAE for comparing each air pollution factor PM2.5, O3, and NO2 with the ground truth.
+- For the Personal Air Quality Prediction with lifelog data subtask: We use the SMAPE/RMSE/MAE for comparing predicted AQI to the ground truth.
+
+The formulation for computing AQI value from (PM2.5, O3, and NO2) data can be found at
+https://en.wikipedia.org/wiki/Air_quality_index (prefer the "Computing the AQI" section)
+
+https://airtw.epa.gov.tw/ENG/Information/Standard/AirQualityIndicator.aspx (prefer the "real-time table" that is the look-up table for C_low, C_high, I_low, I_high value)
#### References and recommended reading
+
+
+[1] Sato, T., Dao, M.S., Kuribayashi, K., and Zettsu, K.: [SEPHLA: Challenges and Opportunities within Environment – Personal Health Archives](https://link.springer.com/chapter/10.1007/978-3-030-05710-7_27), MMM 2018.
+
+[2] P. Zhao and K. Zettsu, [Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution](https://ieeexplore.ieee.org/document/9006604), 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 5620-5629.
+
+[3] N. Nguyen, M. Dao and K. Zettsu, [Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data](https://ieeexplore.ieee.org/abstract/document/9005985), 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 1669-1674.
+[4] P. Vo, T. Phan, M. Dao and K. Zettsu, [Association Model between Visual Feature and AQI Rank Using Lifelog Data](https://ieeexplore.ieee.org/document/90056360), 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 4197-4200.
-### Big Picture of the Task
+[5] Dao, M. S., Zhao, P., Sato, T., Zettsu, K., Dang-Nguyen D. T., Gurrin, C., Nguyen, N. T.: Overview of MediaEval 2019: Insights for Wellbeing TaskMultimodal Personal Health Lifelog Data Analysis, MediaEval Benchmarking Initiative for Multimedia Evaluation (MediaEval 2019), Antipolis, France (November 2019).
-#### Innovation
+[6] Song, H., Lane, K. J., Kim, H., Kim, H., Byun, G., Le, M., Choi, Y., Park, C. R., & Lee, J. T. (2019). [Association between Urban Greenness and Depressive Symptoms: Evaluation of Greenness Using Various Indicators](https://pubmed.ncbi.nlm.nih.gov/30634488/), International journal of environmental research and public health, 16(2), 173.
+[7] Darshan Santani, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2018. [Looking South: Learning Urban Perception in Developing Cities](https://dl.acm.org/doi/10.1145/3224182), Trans. Soc. Comput. 1, 3, Article 13 (December 2018), 23 pages.
-#### Focus
+[8] Anh-Vu Mai-Nguyen, Trong-Dat Phan, Anh-Khoa Vo, Van-Luon Tran, Minh-Son Dao, and Koji Zettsu. 2020. [BIDAL-HCMUS@LSC2020: An Interactive Multimodal Lifelog Retrieval with Query-to-Sample Attention-based Search Engine](https://dl.acm.org/doi/10.1145/3379172.3391722), In Proceedings of the Third Annual Workshop on Lifelog Search Challenge (LSC ’20). Association for Computing Machinery, New York, NY, USA, 43–49.
+[9] Tan-Loc Nguyen-Tai, Dang-Hieu Nguyen, Minh-Tam Nguyen, Thanh-Duong Nguyen, Thanh-Hai Dang, and Minh-Son Dao. 2020. [MNR-HCM Data: A Personal Lifelog and Surrounding Environment Dataset in Ho-Chi-Minh City, Viet Nam](https://dl.acm.org/doi/10.1145/3379174.3392320), In Proceedings of the 2020 Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR ’20). Association for Computing Machinery, New York, NY, USA, 21–26.
-#### Risk management
+[10] Vahdatpour, M., Sajedi, H. & Ramezani, F. [Air pollution forecasting from sky images with shallow and deep classifiers](https://link.springer.com/article/10.1007/s12145-018-0334-x), Earth Sci Inform 11, 413–422 (2018).
+#### Task Organizers,
+Minh-Son Dao (NICT, Japan) dao (at) nict.go.jp
-#### Task organization team
+Peijiang Zhao (NICT, Japan) dlzpj (at) nict.go.jp
+Ngoc-Thanh Nguyen (UIT, Vietnam) thanhnn.13 (at) grad.uit.edu.vn
-#### Task organizers
+Thanh-Binh Nguyen (HCMUS, Vietnam) ngtbinh (at) hcmus.edu.vn
+Duc-Tien Dang-Nguyen (UiB, Norway) ductien.dangnguyen (at) uib.no
-#### Task auxiliaries
+Cathal Gurrin (DCU, Ireland) cgurrin (at) computing.dcu.ie
+#### Task Auxiliaries
+
+Tan-Loc Nguyen-Tai (UIT, Vietnam)
+
+Dang-Hieu Nguyen (UIT, Vietnam)
+
+Minh-Tam Nguyen (UIT, Vietnam)
+
+Quoc-Dat Duong (HCMUS, Vietnam)
+
+Minh-Quan Le (HCMUS, Vietnam)
+
+Trong-Dat Phan (HCMUS, Vietnam)
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~30 October~~ 16 November: Runs due + Start writing Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop (Fully virutal)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/medico.md b/_editions/2020/tasks/medico.md
index 48077f82a..8237db040 100644
--- a/_editions/2020/tasks/medico.md
+++ b/_editions/2020/tasks/medico.md
@@ -5,58 +5,85 @@ year: 2020
hide: false
# required info
-title: Medico 2020
-subtitle:
-blurb: The fight against colorectal cancer requires better diagnosis tools. Computer-aided diagnosis systems can reduce the chance that diagnosticians overlook a polyp during a colonoscopy. This task focuses on robust and efficient algorithms for polyp segmentation. The data consists of a large number of endoscopic images of the colon.
+title: Medico
+subtitle: Semantic polyp segmentation
+blurb: "The fight against colorectal cancer requires better diagnosis tools. Computer-aided diagnosis systems can reduce the chance that diagnosticians overlook a polyp during a colonoscopy. This task focuses on robust and efficient algorithms for polyp segmentation. The data consists of a large number of endoscopic images of the colon."
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Challenge Description
+The "Medico automatic polyp segmentation challenge" aims to develop computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller or flat polyps) with high efficiency and accuracy. The main goal of the challenge is to benchmark semantic segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization.
-#### Introduction
+Participants will get access to a dataset consisting of 1,000 segmented polyp images from the gastrointestinal tract and a separate testing dataset. The challenge consists of two mandatory tasks, each focused on a different requirement for efficient polyp detection. We hope that this task encourages multimedia researchers to apply their vast knowledge to the medical field and make an impact that may affect real lives.
+#### Task Description
+The participants are invited to submit the results on the following tasks:
-#### New for 2020
+1) Polyp segmentation task (required) - The polyp segmentation task asks participants to develop algorithms for segmenting polyps on a comprehensive dataset.
+2) Algorithm efficiency task (required) - The algorithm efficiency task is similar to task one but puts a stronger emphasis on the algorithm's speed in terms of frames-per-second. To ensure a fair evaluation, **this task requires participants to submit a Docker image** so that all algorithms are evaluated on the same hardware.
-#### Target group
+#### Motivation and Background
+Colonoscopy is currently the gold-standard medical procedure for examining the colon for lesions and other abnormalities such as cancer. Colorectal cancer (CRC) is the third most prevailing strain in terms of cancer incidence and second in terms of mortality globally. As early detection is critical for patient survival, regular screening through colonoscopy is a prerequisite for cancer detection and colorectal cancer prevention. Regardless of the success of colonoscopy, it is still estimated to miss around 20% of polyps. This is mostly due to the heavy reliance on the skill of the clinician operating the endoscope and his/her ability to detect polyps. An automated computer-aided diagnosis (CAD) system could be one of the potential solutions for the overlooked polyps. Such detection or surveillance systems can give doctors a so-called "third-eye", thereby alerting them of missed polyps or other common lesions.
+#### Target Group
+The task is of interest to the researchers working with multimedia segmentation, deep learning (semantic segmentation), and computer vision. We especially encourage young researchers to contribute to the field of endoscopy by developing an automated computer-aided diagnosis system that could be potentially used in clinical settings.
#### Data
+The dataset contains 1,000 polyp images and their corresponding ground truth mask. The datasets were collected from real routine clinical examinations at Vestre Viken Health Trust (VV) in Norway by expert gastroenterologists. The VV is the collaboration of the four hospitals that provide healthcare service to 470,000 peoples. The resolution of images varies from 332✕487 to 1920✕1072 pixels. Some of the images contain green thumbnail in the lower-left corner of the images showing the position marking from the ScopeGuide (Olympus). The training dataset can be downloaded from [https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/).
-
-#### Ground Truth
-
+The test dataset is now released. It can be downloaded from [https://drive.google.com/file/d/1uP2W2g0iCCS3T6Cf7TPmNdSX4gayOrv2/view?usp=sharing](https://drive.google.com/file/d/1uP2W2g0iCCS3T6Cf7TPmNdSX4gayOrv2/view?usp=sharing).
#### Evaluation Methodology
+The task will use mean Intersection over Union (mIoU) or Jaccard index as an evaluation metric, which is a standard metric for all medical segmentation task. If the teams have the same mIoU values, then the teams will be further evaluated on the basis of the higher value of the dice coefficient. We strongly recommend calculating other standard evaluation metrics such as dice coefficient, precision, recall, F2, and frame per second (FPS) for a comprehensive evaluation.
+In the challenge overview paper, the organizers will be calculating the metrics such as the Dice coefficient, mIoU, Recall, Precision,Overlap, F2, MAE, FPS, s-score, and Clinical relevance of the methods submitted by each team.
-#### References and recommended reading
+#### Submission
+The submissions will be verified by inspecting the corresponding Docker image. If you have a problem with submitting the Docker image, we are flexible to accept a zip file that contains the predicted mask for task 1. For task 2, we require the Docker submission so that we can evaluate on the same hardware. For the more instruction about the Docker submission, please refer to our [GitHub](https://github.com/DebeshJha/Medico-automatic-polyp-segmentation-challenge/tree/master/submission) repository.
-### Big Picture of the Task
+#### Rules
+By registering this challenge, each individual or team agrees to use only the provided dataset. After the challenge, the dataset test dataset will be made available and the authors can only use it for publication or any non-commercial use. A participating team will be allowed to make a maximum of 5 submissions. The test data can not be used while training the model. The results will be evaluated by the organizers and presented at MediaEval 2020.
-#### Innovation
+#### Discord Channel
+To facilitate communication within the participants, we have set up a Discord channel. You can use this channel for discussion of the challenge or ask questions. Please email debesh@simula.no for the invitation at the Discord.
-#### Focus
-
+#### References and recommended reading
+
+
-#### Risk management
+1. Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D. Johansen. 2020. [Kvasir-seg: A segmented polyp dataset](https://link.springer.com/chapter/10.1007/978-3-030-37734-2_37), In Proceeding of International Conference on Multimedia Modeling (MMM), 451-462.
+2. Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D. Johansen. 2019. [Resunet++: An advanced architecture for medical image segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8959021), In International Symposium on Multimedia (ISM), 225-2255.
-#### Task organization team
+3. Tobias Ross et al. 2020. [Robust Medical Instrument Segmentation Challenge 2019](https://arxiv.org/abs/2003.10299), arXiv preprint.
+4. Sharib Ali et al. 2020. [An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy](https://www.nature.com/articles/s41598-020-59413-5.pdf), Scientific Reports.
-#### Task organizers
+#### Task Organizers
+
-#### Task auxiliaries
+* Debesh Jha, SimulaMet debesh (at) simula.no,
+* Steven Hicks, SimulaMet steven (at) simula.no, SimulaMet
+* Michael Riegler, SimulaMet
+* Pål Halvorsen, SimulaMet and OsloMet
+* Konstantin Pogorelov, Simula Research Laboratory
+* Thomas de Lange, Sahlgrenska University Hospital, Mölndal, Sweden, and Bærum Hospital, Vestre Viken, Norway.
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 01 July: Data release
+* 01 September: Test Data release
+* ~~31 October~~ 16 November: Runs due
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
diff --git a/_editions/2020/tasks/memorability.md b/_editions/2020/tasks/memorability.md
index 9273a3d3c..196dbc778 100644
--- a/_editions/2020/tasks/memorability.md
+++ b/_editions/2020/tasks/memorability.md
@@ -5,71 +5,85 @@ year: 2020
hide: false
# required info
-title: The 2020 Predicting Media Memorability Task
-subtitle: Predicting Media Memorability
-blurb: Media platforms such as social networks, media advertisement, information retrieval and recommendation systems deal with exponentially growing data day after day. Enhancing the relevance of multimedia occurrences in our everyday life requires new ways to organize – in particular, to retrieve – digital content. Like other metrics of video importance, such as aesthetics or interestingness, memorability can be regarded as useful to help make a choice between competing videos. This is even truer when one considers the specific use cases of creating commercials or creating educational content. Because the impact of different multimedia content, images or videos, on human memory is unequal, the capability of predicting the memorability level of a given piece of content is obviously of high importance for professionals in the field of advertising. Beyond advertising, other applications, such as filmmaking, education, content retrieval, etc., may also be impacted by the proposed task.
+title: Predicting Media Memorability
+subtitle:
+blurb: "The task requires participants to automatically predict memorability scores for videos, that reflect the probability for a video to be remembered. Participants will be provided with an extensive data set of videos with memorability annotations, related information, and pre-extracted state-of-the-art visual features."
---
-### Task Description
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-#### Introduction
+#### News
+* 19 October: Latest task schedule published
+* 12 October: Testing set videos and features have been released
+* 30 September: More annotations for the training set have been released
+#### Help for Annotations
+We need more annotations for the dataset. We kindly ask for your help to get more annotations. Please visit the [link](https://annotator.uk/mediaeval/index.php) and participate in the funny game to contribute to the dataset. Thanks in advance for your contribution.
+
+#### Task Description
Media platforms such as social networks, media advertisement, information retrieval and recommendation systems deal with exponentially growing data day after day. Enhancing the relevance of multimedia occurrences in our everyday life requires new ways to organize – in particular, to retrieve – digital content. Like other metrics of video importance, such as aesthetics or interestingness, memorability can be regarded as useful to help make a choice between competing videos. This is even truer when one considers the specific use cases of creating commercials or creating educational content. Because the impact of different multimedia content, images or videos, on human memory is unequal, the capability of predicting the memorability level of a given piece of content is obviously of high importance for professionals in the field of advertising. Beyond advertising, other applications, such as filmmaking, education, content retrieval, etc., may also be impacted by the proposed task.
The task requires participants to automatically predict memorability scores for videos, that reflect the probability for a video to be remembered. Participants will be provided with an extensive data set of videos with memorability annotations, related information, and pre-extracted state-of-the-art visual features.
-#### New for 2020
+#### Background and Motivation
+Understanding what makes a video memorable has a very broad range of current applications, e.g., education and learning, content retrieval and search, content summarization, storytelling, targeted advertising, content recommendation and filtering. Efficient memorability prediction models will also push forward the semantic understanding of multimedia content, by putting human cognition and perception in the center of the scene understanding.
+
In this 3rd edition of the task, a more robust collection of videos is provided, which is retrieved from the TREC Video Retrieval Evaluation (TRECVID) task. Optionally, we may use descriptive captions from their use in the TRECVid automatic video captioning task.
-#### Target group
+#### Target Group
Researchers will find this task interesting if they work in the areas of human perception and scene understanding, such as image and video interestingness, memorability, attractiveness, aesthetics prediction, event detection, multimedia affect and perceptual analysis, multimedia content analysis, machine learning (though not limited to).
#### Data
-Data is composed of 6,000 short videos retrieved from TRECVid. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task [6], memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorization of the videos (short term), and then 24 to 72 hours later (long term). The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Aesthetic Features, C3D, Captions, Colour Histograms, HMP, HoG, Fc7 layer from InceptionV3, LBP, or ORP. In comparison to the videos used in this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view.
+Data is composed of 6,000 short videos retrieved from TRECVid 2019 Video to Text dataset [1]. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task [2], memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorization of the videos (short term), and then 24 to 72 hours later (long term).
-#### Ground Truth
-The ground truth for memorability will be collected through recognition tests, and thus results from objective measures of memory performance.
+Now, a subset of dataset is available including 590 videos as part of the training set. The ground truth of the development data will be enhanced with more annotators per movie with the release of the test data. This would allow to experiment whether increasing the annotations' agreement has a direct influence on the prediction quality. Nevertheless, methods should cope with a lower annotator agreement, which is specific to such subjective tasks.
+
+The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Aesthetic Features, C3D, Captions, Colour Histograms, HMP, HoG, Fc7 layer from InceptionV3, LBP, or ORP. In comparison to the videos used in this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view.
#### Evaluation Methodology
+The ground truth for memorability will be collected through recognition tests, and thus results from objective measures of memory performance.
+
The outputs of the prediction models – i.e., the predicted memorability scores for the videos – will be compared with ground truth memorability scores using classic evaluation metrics (e.g., Spearman’s rank correlation).
#### References and recommended reading
-[1] Aditya Khosla, Akhil S Raju, Antonio Torralba, and Aude Oliva. 2015. Understanding and predicting image memorability at a large scale. In Proc. IEEE Int. Conf. on Computer Vision (ICCV). 2390–2398.\\
-[2] Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva. 2014. What makes a photograph memorable? IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 7 (2014), 1469–1482.\\
-[3] Hammad Squalli-Houssaini, Ngoc Duong, Gwenaëlle Marquant, and Claire-Hélène Demarty. 2018. Deep learning for predicting image memorability. In Proc. IEEE Int. Conf. on Audio, Speech and Language Processing (ICASSP).\\
-[4] Junwei Han, Changyuan Chen, Ling Shao, Xintao Hu, Jungong Han, and Tianming Liu. 2015. Learning computational models of video memorability from fMRI brain imaging. IEEE transactions on cybernetics 45, 8 (2015), 1692–1703.\\
-[5] Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, and Akhil Shetty. 2017. Show and Recall: Learning What Makes Videos Memorable. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2730–2739.\\
-[6] Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. "VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability." Proceedings of the IEEE International Conference on Computer Vision. 2019.\\
-[7] M.G. Constantin, M. Redi, G. Zen, B. Ionescu, "Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates", ACM Computing Surveys, 52(2), 2019.
+
+
+[1] Awad, G., Butt, A.A., Lee, Y., Fiscus, J., Godil, A., Delgado, A., Smeaton, A.F. and Graham, Y., [Trecvid 2019: An evaluation campaign to benchmark video activity detection, video captioning and matching, and video search & retrieval](https://www-nlpir.nist.gov/projects/tvpubs/tv19.papers/tv19overview.pdf). 2019.
-### Big Picture of the Task
+[2] Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://openaccess.thecvf.com/content_ICCV_2019/papers/Cohendet_VideoMem_Constructing_Analyzing_Predicting_Short-Term_and_Long-Term_Video_Memorability_ICCV_2019_paper.pdf). Proceedings of the IEEE International Conference on Computer Vision. 2019.
-#### Innovation
-The computational understanding of video memorability (VM) follows on from the study of image memorability prediction which has attracted increasing attention since the seminal work of Isola et al. [2]. Models achieved very good results at predicting image memorability [1, 3]. In contrast, research on VM from a computer science point of view is in its early stage. The scarcity of studies on VM can be explained by several reasons. Firstly, there is no publicly available data set to train and test models. The second point, closely related to the previous one, is the lack of a common definition for VM. Regarding modelling, the previous attempts at predicting VM [5, 6] highlighted several features which contribute to the prediction of VM, such as semantic, saliency and color features, but the work is far from complete and our capacity to propose efficient computational models will help to meet the challenge of VM prediction. The goal of this task is to participate in the harmonization and the advancement of this emerging search field. Furthermore, in contrast to previous work on image memorability prediction, where memorability was measured a few minutes after memorization, we propose a dataset with “long-term” memorability annotations. We expect the predictions of the models trained on this data to be more representative of long-term memory, which is used preferably in numerous applications.
+[3] Aditya Khosla, Akhil S Raju, Antonio Torralba, and Aude Oliva. 2015. [Understanding and predicting image memorability at a large scale](https://people.csail.mit.edu/khosla/papers/iccv2015_khosla.pdf). In Proc. IEEE Int. Conf. on Computer Vision (ICCV). 2390–2398.
-#### Focus
-Understanding what makes a video memorable has a very broad range of current applications, e.g., education and learning, content retrieval and search, content summarization, storytelling, targeted advertising, content recommendation and filtering. Efficient memorability prediction models will also push forward the semantic understanding of multimedia content, by putting human cognition and perception in the center of the scene understanding.
+[4] Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva. 2014. [What makes a photograph memorable?](http://web.mit.edu/phillipi/www/publications/memory_pami.pdf) IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 7 (2014), 1469–1482.
-#### Risk management
-The task has been successfully organised in 2018 and 2019. The experience gained will help us to anticipate and overcome the inherent difficulties in organizing such a task.
+[5] Hammad Squalli-Houssaini, Ngoc Duong, Gwenaëlle Marquant, and Claire-Hélène Demarty. 2018. [Deep learning for predicting image memorability](https://hal.archives-ouvertes.fr/hal-01629297/file/main.pdf). In Proc. IEEE Int. Conf. on Audio, Speech and Language Processing (ICASSP).
-#### Task organization team
+[6] Junwei Han, Changyuan Chen, Ling Shao, Xintao Hu, Jungong Han, and Tianming Liu. 2015. [Learning computational models of video memorability from fMRI brain imaging](https://ieeexplore.ieee.org/abstract/document/6919270). IEEE transactions on cybernetics 45, 8 (2015), 1692–1703.
-The task benefits from a team from three different research sites and countries, and from different research fields, that have complementary expertise. Most of the organizers already have experience in organizing tasks in the context of the MediaEval and ImageCLEF, and are experts in their fields. University of Essex and University Politehnica of Bucharest are in charge of the creation of the video-based subtask collection. Otherwise, the different teams – University of Essex, University Politehnica of Bucharest and InterDigital – will be involved and will interact in all the aspects of the task (management, dissemination, etc.).
+[7] Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, and Akhil Shetty. 2017. [Show and Recall: Learning What Makes Videos Memorable](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w40/Shekhar_Show_and_Recall_ICCV_2017_paper.pdf). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2730–2739.
-#### Task organizers
+[8] M.G. Constantin, M. Redi, G. Zen, B. Ionescu, [Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates](http://campus.pub.ro/lab7/bionescu/index_files/pub/2018_ACM_CSUR-draft.pdf), ACM Computing Surveys, 52(2), 2019.
-Alba García Seco de Herrera, Rukiye Savran Kiziltepe, Faiyaz Doctor University of Essex, UK;\\
-Mihai Gabriel Constantin, Bogdan Ionescu, University Politehnica of Bucharest, Romania;\\
-Alan Smeaton, Graham Healy, Dublin City University, Ireland;\\
-Claire-Hélène Demarty, InterDigital, R&I, France.
+#### Task organizers
+* Alba García Seco de Herrera, alba.garcia (at) essex.ac.uk, University of Essex, UK
+* Rukiye Savran Kiziltepe, rs16419 (at) essex.ac.uk, University of Essex, UK
+* Faiyaz Doctor, fdocto (at) essex.ac.uk, University of Essex, UK
+* Mihai Gabriel Constantin, cmihaigabriel (at) gmail.com, University Politehnica of Bucharest, Romania
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+* Alan Smeaton, Dublin City University, Ireland
+* Claire-Hélène Demarty, InterDigital, R&I, France
#### Task auxiliaries
-Jon Chamberlain, University of Essex, UK.
+* Jon Chamberlain, University of Essex, UK
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* ~~21~~ 31 August: Data release
+* ~~15~~ ~~31 October~~ 16 November: Runs due
+* ~~15~~ 23 November: Results returned
+* 30 November: Working notes paper
+* Early December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
+
diff --git a/_editions/2020/tasks/music.md b/_editions/2020/tasks/music.md
old mode 100644
new mode 100755
index db1b048c6..eaf84d29d
--- a/_editions/2020/tasks/music.md
+++ b/_editions/2020/tasks/music.md
@@ -2,61 +2,83 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: Emotions and Themes in Music
+subtitle: Emotion and Theme Recognition in Music using Jamendo
+blurb: We invite the participants to try their skills in building a classifier to predict the emotions and themes conveyed in a music recording, using our dataset of music audio, pre-computed audio features, and tag annotations (e.g., happy, sad, melancholic). All data we provide comes from Jamendo, an online platform for music under Creative Commons licenses.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
-#### Introduction
+Emotion and theme recognition is a popular task in music information retrieval that is relevant for music search and recommendation systems. We invite the participants to try their skills at recognizing moods and themes conveyed by the audio tracks.
+This task involves the prediction of moods and themes conveyed by a music track, given the raw audio. The examples of moods and themes are: happy, dark, epic, melodic, love, film, space etc. Each track is tagged with at least one tag that serves as a ground-truth.
-#### New for 2020
+Participants are expected to train a model that takes raw audio as an input and outputs the predicted tags. To solve the task, participants can use any audio input representation they desire, be it traditional handcrafted audio features or spectrograms or raw audio inputs for deep learning approaches. We also provide a handcrafted feature set extracted by the [Essentia](https://essentia.upf.edu/documentation/) audio analysis library as a reference. We allow usage of third-party datsets for model development and training, but it needs to be mentioned explicitly.
+
-#### Target group
+#### Target Group
+Researchers in music information retrieval, music psychology, machine learning, and music and technology enthusiasts in general.
#### Data
+The dataset used for this task is the `autotagging-moodtheme` subset of the [MTG-Jamendo dataset](https://github.com/MTG/jamendo-dataset) [1], built using audio data from [Jamendo](https://jamendo.com) and made available under Creative Commons licenses. This subset includes 18,486 audio tracks with mood and theme annotations. In total, there are 57 tags, and tracks can possibly have more than one tag.
-#### Ground Truth
+We also provide pre-computed statistical features from [Essentia](https://essentia.upf.edu) using the feature extractor for [AcousticBrainz](https://acousticbrainz.org/). These features are were previously used in the MediaEval genre recognition tasks in [2017](https://multimediaeval.github.io/2017-AcousticBrainz-Genre-Task/) and [2018](https://multimediaeval.github.io/2018-AcousticBrainz-Genre-Task/).
#### Evaluation Methodology
+Participants should generate predictions for the [test split](https://github.com/MTG/jamendo-dataset/blob/master/data/splits/split-0/autotagging_moodtheme-test.tsv) and submit those to the task organizers.
-#### References and recommended reading
+The generated outputs for the test dataset will be evaluated according to the following metrics that are commonly used in the evaluation of auto-tagging systems: Macro **ROC-AUC** and **PR-AUC** on tag prediction scores. Leaderboard will be based on PR-AUC.
+For reference, [here](https://multimediaeval.github.io/2019-Emotion-and-Theme-Recognition-in-Music-Task/) is the website of the 2019 edition of the task.
-### Big Picture of the Task
-#### Innovation
+#### References and recommended reading
+
+
+[1] Dmitry Bogdanov, Minz Won, Philip Tovstogan, Alastair Porter and Xavier Serra. 2019. [The MTG-Jamendo dataset for automatic music tagging](http://mtg.upf.edu/node/3957). Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019).
-#### Focus
+[2] Dmitry Bogdanov, Alastair Porter, Philip Tovstogan and Minz Won. 2019. [MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo](https://github.com/multimediaeval/2019-Emotion-and-Theme-Recognition-in-Music-Task/blob/master/jamendo-emotion-theme-task-me19.pdf). MediaEval 2019 Workshop.
+[3] Mohammad Soleymani, Micheal N. Caro, Erik M. Schmidt, Cheng-Ya Sha and Yi-Hsuan Yang. 2013. [1000 songs for emotional analysis of music](https://ibug.doc.ic.ac.uk/media/uploads/documents/cmm13-soleymani.pdf). In Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia (CrowdMM 2013), 1-6.
-#### Risk management
+[4] Anna Aljanaki, Yi-Hsuan Yang and Mohammad Soleymani. 2014. [Emotion in music task at MediaEval 2014](http://ceur-ws.org/Vol-1263/mediaeval2014_submission_33.pdf).
+[5] Renato Panda, Ricardo Malheiro and Rui Pedro Paiva. 2018. [Musical texture and expressivity features for music emotion recognition](http://mir.dei.uc.pt/pdf/Conferences/MOODetector/ISMIR_2018_Panda.pdf). In Proceedings of the International Society on Music Information Retrieval Conference (ISMIR 2018), 383-391.
-#### Task organization team
+[6] Cyril Laurier, Owen Meyers, Joan Serra, Martin Blech and Perfecto Herrera. 2009. [Music mood annotator design and integration](http://mtg.upf.edu/files/publications/Laurier_MusicMoodAnnotator.pdf). In 7th International Workshop on Content-Based Multimedia Indexing (CBMI'09), 156-161.
+[7] Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Brandon G. Morton, Patrick Richardson, Jeffrey Scott, Jacquelin A. Speck and Douglas Turnbull. 2010. [Music emotion recognition: A state of the art review](http://ismir2010.ismir.net/proceedings/ismir2010-45.pdf). In Proceedings of the International Society on Music Information Retrieval Conference (ISMIR2010), 255-266.
-#### Task organizers
+[8] Xiao Hu and J. Stephen Downie. 2007. [Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata](http://ismir2007.ismir.net/proceedings/ISMIR2007_p067_hu.pdf). In Proceedings of the International Conference on Music Information Retrieval (ISMIR2007), 67-72.
-#### Task auxiliaries
+#### Task Organizers
+Philip Tovstogan, Music Technology Group, Universitat Pompeu Fabra, Spain
+Dmitry Bogdanov, Music Technology Group, Universitat Pompeu Fabra, Spain
+Alastair Porter, Music Technology Group, Universitat Pompeu Fabra, Spain
+Minz Won, Music Technology Group, Universitat Pompeu Fabra, Spain
+(first.last@upf.edu)
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 13 July: Data release
+* 6 November: Runs due
+* 15 November: Results returned
+* 30 November: Working notes paper
+* Early December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
diff --git a/_editions/2020/tasks/newsimages.md b/_editions/2020/tasks/newsimages.md
index db1b048c6..900d6fe49 100644
--- a/_editions/2020/tasks/newsimages.md
+++ b/_editions/2020/tasks/newsimages.md
@@ -2,61 +2,85 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "NewsImages: The role of images in online news"
+subtitle:
+blurb: Images play an important role in online news articles and news consumption patterns. This task aims to achieve additional insight about this role. Participants are supplied with a large set of articles (including text body, and headlines) and the accompanying images. The task requires participants to predict which image was used to accompany each article and also predict frequently clicked articles on the basis of accompanying images.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+News articles use both text and images to communicate their message. The overall goal of this task is to better understand the relationship the textual content of the articles (including text body, and headlines) and to understand the impact of these elements on readers’ interest in the news (measured by the number of views)
-#### Introduction
+Participants receive a data set of news articles and accompanying images. The news articles consist of text snippets (first 256 characters) and headlines. They can participate in either (or both) of two subtasks.
+##### Task 1: Image-Text Re-Matching
-#### New for 2020
+News articles contain images that accompany the text. The connection between the images and text is more complex than often realized. In this task, participants predict (from a set of images) which image was published with a given news article. We also ask participants to report their insights into characteristics that connect the text of news articles and the images.
+##### Task 2: News Click Prediction
-#### Target group
+News websites present users with recommendations of what to read next. These are often displayed as the article title plus an accompanying image. In this task, participants investigate whether recommendations that are frequently clicked by users can be predicted using the text content of the article and/or the accompanying image.
+Participants are encouraged to make their code public with their submission.
-#### Data
+#### Motivation and Background
+Online news articles are multimodal: the textual content of an article is often accompanied by an image. The image is important for illustrating the content of the text, but also attracting readers' attention. Research in multimedia and recommender systems generally assumes a simple relationship between images and text occurring together. For example, in image captioning [6], the caption is often assumed to describe the literally depicted content of the image. In contrast, when images accompany news articles, the relationship becomes more complex [8]. The goal of this task is to investigate this complexity in more depth, in order to understand the implications that it may have for the areas of journalism and recommender systems.
+The task is formulated into two straightforward subtasks that participants can address using text-based and/or image features. However, the ultimate objective of this task is to gain additional insight. Specifically, we are curious about the connection between the textual content of articles and the images that accompany them and also about the connection between the image and title shown by a recommender system to users and the tendency of users to click on the recommended article. We are especially interested in aspects of images that go beyond the conventional set of concepts studied by concept detection. We are also interested in aspects of images that go beyond the literally depicted content. Such aspects include color, style and framing.
-#### Ground Truth
+#### Target Group
+This task targets researchers who are interested in the connection between images and text and images and user consumption behavior. This includes people working in the areas of computer vision, recommender systems, cross-modal information retrieval, as well as in the area of news analysis.
+#### Data
+The data set is a large collection of news articles (ca. 10k-15k) from a German publisher that publishes news article recommendations on its website. Each article consists of a headline and a text snippet (first 256 characters) plus the link to download the accompanying image. The data is split into a training set (ground truth provided) and a test set. Participants must crawl their own images. To strictly ensure fair comparison, the final test set will include the test set articles for which all participants could successfully access the images.
+In order to simplify the image analysis, we provide image annotations computed using standard image annotation models (e.g. VGG19) trained on image net. Details about the provided image annoations can be found in the description of the [Multi-Media Recommendation task 2019](http://www.dai-labor.de/fileadmin/Files/Publikationen/Buchdatei/MultiMediaRec2019.pdf) (Section 3.2 - Data).
#### Evaluation Methodology
+##### Task 1: Image-Text Re-Matching
+For each news article in the test set, participants return the top five images that they predict to have accompanied that article. Success is measured with Precision@5.
+##### Task 2: News Click Prediction
+Given a set of news articles, participants predict the topmost news articles that are likely to be clicked when they are recommended. The number of topmost articles will be specified. Success is measured with the number of correct predictions.
+##### Analysis and Insight
+For both tasks, the ultimate goal is to understand news and news consumption behavior. We will also judge participants in terms of the quality of the insight that they achieve about the relationship between text and images and in the relationship between images and news consumption behavior.
#### References and recommended reading
+
+
+[1] Corsini, Francesco, and Martha A. Larson. [CLEF NewsREEL 2016: image based recommendation.](https://repository.ubn.ru.nl/bitstream/handle/2066/161886/161886.pdf) (2016).
+[2] Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007, May). [Google news personalization: scalable online collaborative filtering](https://dl.acm.org/doi/abs/10.1145/1242572.1242610). In Proceedings of the 16th international conference on World Wide Web (pp. 271-280).
-### Big Picture of the Task
-
-#### Innovation
-
+[3] Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., & Huber, A. (2014, October). [Offline and online evaluation of news recommender systems at swissinfo.ch](https://dl.acm.org/doi/abs/10.1145/2645710.2645745). In Proceedings of the 8th ACM Conference on Recommender systems (pp. 169-176).
-#### Focus
+[4] Ge, M., & Persia, F. (2017). [A survey of multimedia recommender systems: Challenges and opportunities.](https://www.worldscientific.com/doi/abs/10.1142/S1793351X17500039) International Journal of Semantic Computing, 11(03), 411-428.
+[5] Hopfgartner, F., Balog, K., Lommatzsch, A., Kelly, L., Kille, B., Schuth, A., & Larson, M. (2019). [Continuous evaluation of large-scale information access systems: a case for living labs.](https://link.springer.com/chapter/10.1007/978-3-030-22948-1_21) In Information Retrieval Evaluation in a Changing World (pp. 511-543). Springer, Cham.
-#### Risk management
+[6] Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019). [A comprehensive survey of deep learning for image captioning.](https://dl.acm.org/doi/abs/10.1145/3295748) ACM Computing Surveys (CSUR), 51(6), 1-36.
+[7] Lommatzsch, A., Kille, B., Hopfgartner, F., Larson, M., Brodt, T., Seiler, J., & Özgöbek, Ö. (2017, September). [CLEF 2017 NewsREEL overview: A stream-based recommender task for evaluation and education.](https://link.springer.com/book/10.1007/978-3-319-65813-1) In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 239-254). Springer, Cham.
-#### Task organization team
+[8] Oostdijk, N., van Halteren, H., Bașar, E., & Larson, M. (2020, May). [The Connection between the Text and Images of News Articles: New Insights for Multimedia Analysis.](https://www.aclweb.org/anthology/2020.lrec-1.535/) In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 4343-4351).
+#### Task Organizers
+* Benjamin Kille, TU Berlin, Germany (benjamin.kille (at) tu-berlin.de)
+* Andreas Lommatzsch, TU Berlin, Germany (andreas.lommatzsch (at) dai-labor.de)
+* Özlem Özgöbek, NTNU Trondheim, Norway
-#### Task organizers
-
-
-#### Task auxiliaries
-
+#### Task Auxiliaries
+* Martha Larson, Radboud University, Netherlands
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~31 October~~ 16 November: Runs due + Start working on Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December : MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/no-audiospeech.md b/_editions/2020/tasks/no-audiospeech.md
deleted file mode 100644
index db1b048c6..000000000
--- a/_editions/2020/tasks/no-audiospeech.md
+++ /dev/null
@@ -1,62 +0,0 @@
----
-# static info
-layout: task
-year: 2020
-hide: true
-
-# required info
-title:
-subtitle:
-blurb:
----
-
-
-
-### Task Description
-
-#### Introduction
-
-
-#### New for 2020
-
-
-#### Target group
-
-
-#### Data
-
-
-#### Ground Truth
-
-
-#### Evaluation Methodology
-
-
-#### References and recommended reading
-
-
-### Big Picture of the Task
-
-#### Innovation
-
-
-#### Focus
-
-
-#### Risk management
-
-
-#### Task organization team
-
-
-#### Task organizers
-
-
-#### Task auxiliaries
-
-
-#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
diff --git a/_editions/2020/tasks/noaudio-speech.md b/_editions/2020/tasks/noaudio-speech.md
new file mode 100644
index 000000000..bd08b0186
--- /dev/null
+++ b/_editions/2020/tasks/noaudio-speech.md
@@ -0,0 +1,104 @@
+---
+# static info
+layout: task
+year: 2020
+hide: false
+
+# required info
+title: "No-Audio Multimodal Speech Detection Task"
+subtitle:
+blurb: "Participants receive videos (top view) and sensor readings (acceleration and proximity) of people having conversations in a natural social setting and are required to detect speaking turns. No audio signal is available for use. The task encourages research on better privacy preservation during recordings made to study social interactions, and has the potential to scale to settings where recording audio may be impractical."
+---
+
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
+
+#### Task Description
+
+Task participants are provided with video of individuals participating in a conversation that was captured by an overhead camera. Each individual is also wearing a badge-like device, recording tri-axial acceleration.
+
+The goal of the task is to automatically estimate when the person seen in the video starts speaking, and when they stop speaking using these alternative modalities. In contrast to conventional speech detection, for this task, no audio is used. Instead, the automatic estimation system must exploit the natural human movements that accompany speech (i.e., speaker gestures, as well as shifts in pose and proximity).
+
+This task consists of two subtasks, with a new optional subtask:
+
+* **Unimodal classification:** Design and implement separate speech detection algorithms exploiting each modality separately: Teams must submit separate decisions for the wearable modality and for the video modality.
+
+* **Multimodal classification:** Design and implement a speech detection approach that integrates modalities. Teams must submit a multimodal estimation decision, using some form of early, late or hybrid fusion.
+
+* **Analysis of failure test cases (optional):** From previous editions, test subjects with lower performances compared to the mean have been discovered. In this sub-task, participants are encouraged to analyze these particular subjects and show or hypothesize about possible reasons for such low performances.
+
+Speaking predictions must be made for every second. However, it is left to the teams if they decide to use a different interval length and later interpolate or extrapolate to the second level.
+
+
+#### Motivation and Background
+
+An important but under-explored problem is the automated analysis of conversational dynamics in large unstructured social gatherings such as networking or mingling events. Research has shown that attending such events contributes greatly to career and personal success [7]. While much progress has been made in the analysis of small pre-arranged conversations, scaling up robustly presents a number of fundamentally different challenges.
+
+This task focuses on analysing one of the most basic elements of social behaviour: the detection of speaking turns. Research has shown the benefit of deriving features from speaking turns for estimating many different social constructs such as dominance, or cohesion to name but a few. Unlike traditional tasks that have used audio to do this, here the idea is to leverage the body movements (i.e. gestures) that are performed during speech production which are captured from video and/or wearable acceleration and proximity. The benefit of this is that it enables a more privacy-preserving method of extracting socially relevant information and has the potential to scale to settings where recording audio may be impractical.
+
+The relationship between body behaviour such as gesturing while speaking has been well-documented by social scientists [1]. Some efforts have been made in recent years to try and estimate these behaviours from a single body-worn triaxial accelerometer, hung around the neck [2,3]. This form of sensing could be embedded into a smart ID badge that could be used in settings such as conferences, networking events, or organizational settings. In other works, video has been used to estimate speaking status [4,5]. Despite these efforts, one of the major challenges has been in getting competitive estimation performance compared to audio-based systems. As yet, exploiting the multi-modal aspects of the problem is an under-explored area that will be the main focus of this challenge.
+
+
+#### Target Group
+
+This challenge is targeted at researchers in wearable devices, computer vision, signal and speech processing. The aim is to provide an entry-level task that has a clearly definable ground truth. There are many nuances to this problem that would enable this problem to be solved better if an intuition behind the behaviour is better understood. The problem could also be solved without this knowledge. The hope, however, is that this task will allow researchers who are not familiar with social signal processing to learn more about the problem domain; we have subsequent challenges in mind in later years that would become increasingly complex in terms of the social context and social constructs that are not so easily understood in terms of their social cue representation (e.g. personality, attraction, conversational involvement). The recommended readings for the challenge are [3,5,6]. Reading references [1,2,4] may provide additional insights on how to solve the problem.
+
+
+#### Data
+
+The data consists of 70 people who attended one of three separate mingle events (cocktail parties). Overhead camera data as well as wearable tri-axial accelerometer data for an interval of 30 minutes is available for this task. Each person used a wearable device (to record the acceleration acceleration) hung around the neck as a conference badge. A subset of this data will be kept as a test set. All the samples of this test set will be for subjects who are not in the training set.
+
+All the data is synchronized. The video data is mostly complete, with some segments missing as the participants can leave the recording area at any time (e.g. to go to the bathroom). The frame rate of the video and sample rate of the accelerometer data are captured at 20Hz. Note that due to the crowded nature of the events, there can be strong occlusions between participants in the video, which we hope to evaluate in one of our sub-tasks.
+
+
+#### Evaluation Methodology
+
+Manual annotations are provided for binary speaking status (speaking / non-speaking) for all people. These annotations are carried out for every frame in video (20 FPS). As mentioned above, speaking predictions must be made for every second.
+
+Since the classes are severely imbalanced, we will be using the Area Under the ROC Curve (ROC-AUC) as the evaluation metric. Thus, participants should submit non-binary prediction scores (posterior probabilities, distances to the separating hyperplane, etc.).
+
+The task will be evaluated using a subset of the data left as a test set. All the samples of this test set will be for subjects who are not present in the training set.
+
+For evaluation, we will ask the teams to provide the following estimations for the two subtasks states above (unimodal and multimodal):
+
+* **Person independent:** All samples are provided to the classifier together, irrespective of the subject that the samples came from. Note that the test samples we provide will samples taken from people who are not in the training data.
+
+* **(optional) Person specific:** Only samples generated from the same subject are provided to the classifier. So we expect participants to train one classifier for each person and output test results per person-specific classifier. This can be a useful sanity check as the performance of the method, which should, in theory, perform better when trained on a specific person rather than other people.
+The data contains occluded and non-occluded moments, and the performance of systems will be also calculated for both of these subsets individually, in order to gain greater insight into the results that participants achieve.
+
+
+#### References and recommended reading
+
+
+[1] McNeill, D.: [Language and gesture](https://www.cambridge.org/core/books/language-and-gesture/2D216A21B6484304C347FFB0DFCC39BB), vol. 2. Cambridge University Press (2000)
+
+[2] Hung, H., Englebienne, G., Kools, J.: [Classifying social actions with a single accelerometer](https://dl.acm.org/doi/pdf/10.1145/2493432.2493513?casa_token=rB1te_mCb3wAAAAA:dTnFrRm1YqAOVqsixGmJu_Xc1fKZQfhLbuju5meZnMMj1C15xzSQ0yBvnE5Nw3SFnSnXdC9ls3ZEC1M). In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 207–210. ACM (2013)
+
+[3] Gedik, E. and Hung, H., [Personalised models for speech detection from body movements using transductive parameter transfer](https://link.springer.com/article/10.1007/s00779-017-1006-4), Journal of Personal and Ubiquitous Computing, (2017)
+
+[4] Hung, H. and Ba, S. O., [Speech/non-speech Detection in Meetings from Automatically Extracted Low Resolution Visual Features](https://infoscience.epfl.ch/record/146060), Idiap Research Report, (2010)
+
+[5] Cristani, M., Pesarin, A., Vinciarelli, A., Crocco, M. , and Murino, V., [Look at who’s talking: Voice activity detection by automated gesture analysis](https://d1wqtxts1xzle7.cloudfront.net/8048683/gestures.pdf?1327801984=&response-content-disposition=inline%3B+filename%3DLook_at_whos_talking_Voice_activity_dete.pdf&Expires=1594664408&Signature=ea8pxw-LIng563aOFzxmlug-7SJqjNvizHJ1UY1kY-ANJ8qq8XS0~EBhOvKVaTT1KgAoducvgJHOdh7md3~jYFqBqcVV7QGsKRt8H5s1Ni0m7yOndhI5Acm6RAJzOUsHCubP3LsyzdClZ5sAP769KLVubpaweNw5uvUJzw8kbOTijVzF7rET4aOmc4FY7m0avFzi4jlYr65kJm5jIG1AOOfY7gycMbhYfJalg4n7C4H2X7Xyt-IqvDfHnpuxSK6Hj4pljfTn8wuFJjt6OTeDmA7jNlyiRMqhpuuvhhoK94N2~Zq1KFe6H4wDGH1BjWSGZwfwkBpL4J3J2BzGJdVCtw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA), In the workshop on Interactive Human Behavior Analysis in Open or Public Spaces, International Joint Conference on Ambient Intelligence, (2011).
+
+[6] Cabrera-Quiros, L., Demetriou, A., Gedik, E., van der Meij, L., & Hung, H. (2018). [The MatchNMingle dataset: a novel multi-sensor resource for the analysis of social interactions and group dynamics in-the-wild during free-standing conversations and speed dates](http://homepage.tudelft.nl/3e2t5/MatchNMingle.pdf). IEEE Transactions on Affective Computing.
+
+[7] Wolff, H.-G. and Moser, K. , [Effects of networking on career success: a longitudinal study](http://homepages.se.edu/cvonbergen/files/2013/01/Effects-of-Networking-on-Career-Success_A-Longitudinal-Study.pdf). Journal of Applied Psychology, 94(1):196, (2009).
+
+
+#### Task Organizers
+
+* Laura Cabrera Quiros, Instituto Tecnológico de Costa Rica, Costa Rica and Delft University of Technology, Netherlands (guest). l dot cabrera at itcr.ac.cr
+* Hayley Hung, Delft University of Technology, Netherlands. h dot hung at tudelft dot nl
+* Jose Vargas-Quiros, Delft University of Technology, Netherlands. J dot D dot VargasQuiros at tudelft dot nl
+
+
+
+#### Task Schedule
+* 21 July: Data release
+* ~~31 October~~ 16 November: Runs due + Start working on Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December : MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/pixelprivacy.md b/_editions/2020/tasks/pixelprivacy.md
index db1b048c6..0a2ef6cd5 100644
--- a/_editions/2020/tasks/pixelprivacy.md
+++ b/_editions/2020/tasks/pixelprivacy.md
@@ -2,61 +2,83 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "Pixel Privacy: Quality Camouflage for Social Images"
+subtitle:
+blurb: In this task, participants develop adversarial approaches that camouflage the quality of images. A camouflaged image appears to be unchanged, or even enhanced, to the human eye. At the same time, the image will fool a Blind Image Quality Assessment algorithm into predicting that its quality is low. Quality camouflage will help to ensure that personal photos, e.g., vacation photos depicting people, are less easily findable via image search engines.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+High-quality images shared online can be misappropriated for promotional goals. In this task, participants work to defeat an automatic image quality classifier, which effectively hides images.
+The camouflaged image appears to be appealing to the human eye, but the Blind Image Quality Assessment (BIQA) classifier finds it to be low quality, i.e., there is a dramatic descrease in the image's automatically predicted quality score.
-#### Introduction
+Participants will receive a set of images (representative of images shared on social media) and are required to modify them. The modification should achieve two goals: (1) Protection: It must block the ability of a binary BIQA classifier from correctly predicting the quality of images and (2) Appeal: The changes made to the image must be as imperceptible to the human eye as possible, or the changes must contribute to enhancing the appeal of the image.
+Note that the task is not focused on concealing sensitive information from humans, rather from automatic inference.
+This year the quality camouflage task is a "whitebox" attack. Participants' goal is to defeat a BIQA that predicts the perceptual quality of images. The BIQA is trained on KonIQ-10k, which contains 10,073 in-the-wild images annotated with subjective quality scores.
-#### New for 2020
+Participants can choose to address the task in one of two different ways. In the first, the quality camouflage approach seeks to make invisible changes to the image. In the second, the approach makes visible changes to the image, but restricts itself to changes that enhance the image’s appeal, or at least do not bother someone looking at the image.
+We encourage participants to release their code.
-#### Target group
+#### Background and Motivation
+Conventionally, adversarial images are studied in the context of scenarios in which they can cause harm by misleading computer vision system. *Pixel Privacy* introduces the notion that adversarial images can serve another goal as well: protect privacy-sensitive information in cases in which the computer vision system itself may be a source of harm. An important example is image search engines that can find images of people who are pictured in a specific location or setting. In the past installment of the task, we have investigated how adversarial images can effectively prevent the inference of semantic information concerning the scene of an image.
+Semantic information, is not, however, the only characteristic that a search engine uses in order to return a results list to a user. Another characteristic is automatically-inferred image quality. In the 2020 Pixel Privacy task we turn our attention to *quality camouflage*, i.e., approaches which can cause a Blind Image Quality Assessment (BIQA) to misclassify an image as low quality, when it appears to be high quality to the human eye. Quality camouflage will help to ensure that personal photos reflecting sensitive scene information, e.g., vacation photos depicting people, are less easily findable via image search engines.
-#### Data
-
-
-#### Ground Truth
+
+#### Target Group
+We hope that this task attracts a wide range of participants who are concerned about privacy from computer scientists to artists and photographers. Within the field of computer science, people interested in machine learning, adversarial machine learning, computer graphics, privacy, and computer vision will find the task interesting.
+#### Data
+Development data will be the KonIQ-10k data set. Test data will be selected from the validation set of the Places365, and different from previous editions, we will use high-resolution images for ensuring relatively high quality. Participants are provided with a set of correctly classified images that are also predicted to have ‘good quality’ by the BIQA model.
#### Evaluation Methodology
+The protection score will be the accuracy of the BIQA prediction on modified images by participants. We specify the threshold that is to be used by BIQA. Note that we expect that accuracy to decrease after protection, but theoretically it is also possible that protection fails, and that it stays the same.
+We appreciate approaches that are robust against practical post-processing operations. So the submitted images will be evaluated on their slightly JPEG compressed (quality factor = 90%) version. We have confirmed such compression has little impact on the BIQA scores.
-#### References and recommended reading
-
-
-### Big Picture of the Task
-
-#### Innovation
+Appeal will be evaluated by a jury of computer vision experts. Submissions will be ranked as follows: All approaches that achieve a protection score of at least 50% (50% reduction in the accuracy of the prediction) will be ranked in terms of their appeal by the jury.
+#### References and recommended reading
+
+
+Vlad Hosu, Hanhe Lin, Tamas Sziranyi and Dietmar Saupe. [KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment](https://ieeexplore.ieee.org/document/8968750). In IEEE Transactions on Image Processing 29, (Jan. 2020), 4041-4056.
-#### Focus
+Samuel Dodge, and Lina Karam. [Understanding How Image Quality Affects Deep Neural Networks](https://ieeexplore.ieee.org/document/7498955). In Proceedings of 8th International Conference on Quality of Multimedia Experience (QoMEX '16). IEEE, 1-6.
+Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.[Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572). International Conference on Learning Representations (ICLR '15).
-#### Risk management
+Pixel Privacy Task. 2018. Working Notes Proceedings of the MediaEval 2018 Workshop. Retrieved from http://ceur-ws.org/Vol-2283/
+Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, and Duen Horng Chau. 2018. [SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression](https://dl.acm.org/doi/10.1145/3219819.3219910). In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18). Association for Computing Machinery, New York, NY, USA, 196–204.
-#### Task organization team
+Zhengyu Zhao, Zhuoran Liu, and Martha Larson. 2020. [Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter](https://arxiv.org/abs/2002.01008). In Proceedings of the 31th British Machine Vision Conference (BMVC '20).
+
-#### Task organizers
+#### Task Organizers
+
+
-#### Task auxiliaries
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~31 October~~ 16 November: Runs due + Start writing Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/scenechange.md b/_editions/2020/tasks/scenechange.md
new file mode 100644
index 000000000..317a22e80
--- /dev/null
+++ b/_editions/2020/tasks/scenechange.md
@@ -0,0 +1,119 @@
+---
+# static info
+layout: task
+year: 2020
+hide: false
+
+# required info
+title: "Scene Change: Fun faux photos"
+subtitle:
+blurb: "Tourist photography is due for a makeover, as people increasingly avoid travel due to environmental or safety concerns. In this task, participants create image composites: given a photo of a person, they must change the background to a popular tourist site. The special twist: a Scene Change photo must be fun without being deceptive. In other words, the photo fools you at first, but is identifiable as a composite upon closer inspection."
+---
+
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
+
+#### Task Description
+The MediaEval 2020 Scene Change Task is interested in exploring fun faux photo’s, images that fool you at first, but can be identified as an imitation on closer inspection. Task participants are provided with images of people (as a “foreground segment”) and are asked to change the background scene to Paris. We call this switch “scene change”.
+
+Based on the dataset provided, participants are asked to develop a system that addresses the main task of creating a composite image:
+
+* **Image compositing**: given a foreground segment and a background image, the participant should blend the segment with the background in an appealing manner. This is done for several popular landmarks in Paris. Only the foreground segment may be manipulated, so that the background image is recognizable as the specific landmark.
+
+Participants are encouraged to improve the systems that address the main task, by developing additional sub-systems:
+
+* **Background image retrieval**: given a foreground segment the participant should retrieve a suitable background image from the collection of background images taken near the same landmark, which is a good fit. Then, the foreground segment should blend with the background image as in the main task with respect to, for example, lighting conditions and perspective.
+
+* **Foreground segmentation**: the foreground segment and the original foreground image is provided. Segmentation has seen some remarkable advances recently, but remains a difficult task, for example with respect to hair. Participants are invited to refine the provided segmentations and gain insights from there.
+
+Note that for this task photorealism is not a goal in and of itself. Similarly to [1], we do strive for realism in the sense of acceptability, which includes enjoyability and shareability, rather than of physical accuracy. Physical accuracy is not required for acceptability, for example it is known that in artistic work impossible lighting conditions and colors do not interfere with the viewer’s understanding of the scene and often go unnoticed [2]. We adopt the assumption that optimizing for this realism captures distracting properties of the composed image, resulting in more appealing final images.
+
+
+
+*Can you tell at first glance who was in Paris? Can you tell at second glance?*
+
+
+#### Motivation and Background
+The task has multiple motivations:
+
+* Access to scene change functionality is currently restricted to a small group including painters, photographers, Adobe® Photoshop® users and computer graphics experts. There is a large gap to bridge in commoditizing scene change. Giving users more control over their own photos will allow them to exercise creativity, have fun and promote their privacy more at the same time. The relatively recent surge of creative tools (e.g. Animoji, Snapchat Lenses) suggests that people enjoy creative control over their images and videos. However, closer consideration of the functionality of these tools reveals limitations: the creative possibilities are potentially so much wider than what is currently available to users.
+
+* More and more examples where large group of tourists, often taking selfies, cause harm to the environment arise [5,6]. Scene change could be a partial solution to this problem, relieving pressure on these popular areas.
+
+* Because of coronavirus travel restrictions, people who love to travel have become creative about replacing travel photography [7,8,9,10]. We want to encourage the trend of "traveling from home" to survive beyond times of coronavirus lockdown in order to make the travel experience available to those with health restrictions, who cannot afford travel, or who wish to fly less for environmental reasons. Currently, services exist, e.g., [https://www.fakeavacation.com/](https://www.fakeavacation.com/), that target fully deceptive photos. Scene Change disassociates itself from this practice, and instead connects itself trend of creating an authentic at-home experience of a travel destination.
+
+* As computer scientists we make methods that allow people fool around with photos in a way that is not fully deceptive. Developing technologies for “shallow fakes” provides an alternative to recent work, aimed at deep deception [11], in which the intent of the creator is that the fabricated image is not recognized as such. By benchmarking, we can evaluate methods and metrics for performing and quantifying deceptiveness in multimedia. If we can find practical methods for doing so, people can enjoy new creations without being deceived into accepting fiction as fact.
+
+The task focuses on Paris, both because it is a highly popular tourist destination and also due to the availability of a Paris Dataset [12]. In 2017, France was the most visited country in the world, with Paris having a total of 23,6 million hotel visits [13,14].
+
+
+#### Target Group
+The task targets (but is not limited to) people interested in art and social media, multimedia retrieval, machine learning, adversarial machine learning, privacy and computer vision.
+
+Depending on your research interests, you might want to experiment in other directions. We have provided a recommended reading list (below) with some suggestions. You might consider using a Generative-Adversarial-Network based approach, for instance building on the work of Lin et al. 2018. You could also try an approach similar to that of Lalonde et al. 2007, who retrieve foreground segments that match certain conditions to the background.
+
+
+#### Data
+The data will be drawn from the ADE20k [4] dataset and the Paris dataset.
+
+
+#### Evaluation Methodology
+Participants submit scene change examples for all images in the test set. The scene change is evaluated in an user study, where study participants are randomly shown original and composed images and are asked to point out which image was originally taken at the location. The study is repeated twice, once time-restricted, similar to [3] and once unrestricted. A good algorithm produces shallow fakes: it demonstrates a high error rate on the time-restricted experiment and a low error rate on the unbounded experiment. Submissions are ranked on the difference in error rates between the two experiments.
+
+
+#### References
+
+
+
+
+[1] Karsch, K., Hedau, V., Forsyth, D., & Hoiem, D. (2011). [Rendering synthetic objects into legacy photographs](https://dl.acm.org/doi/10.1145/2070781.2024191). ACM Transactions on Graphics (TOG), 30(6), 157.
+
+[2] Cavanagh, P. (2005). [The artist as neuroscientist](https://www.nature.com/articles/434301a). Nature, 434(7031), 301.
+
+[3] Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song. [Spatially Transformed Adversarial Examples](https://openreview.net/forum?id=HyydRMZC-). ICLR 2018.
+
+[4] Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017). [Scene parsing through ade20k dataset](https://ieeexplore.ieee.org/document/8100027). In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 633-641).
+
+[5] Roy, E. A. (2018, December 06). Instacrammed: The big fib at the heart of New Zealand picture-perfect peaks. The Guardian. Retrieved from https://www.theguardian.com/world/2018/dec/07/instacrammed-the-big-fib-at-the-heart-of-new-zealand-picture-perfect-peaks
+
+[6] Gammon, K. (2019, March 19). #Superbloom or #poppynightmare? Selfie chaos forces canyon closure. The Guardian. Retrieved from https://www.theguardian.com/environment/2019/mar/18/super-bloom-lake-elsinore-poppies-flowers
+
+[7] Rogers,K. (2020, March 20) Coronavirus canceled this family's Disney trip. They made better memories at home. CNN. Retrieved from https://edition.cnn.com/travel/article/texas-family-disney-world-coronavirus/index.html
+
+[8] Compton, N.B. (2020, April 8) Travel photographers are taking epic nature photos using indoor optical illusions. Washington Post. Retrieved from https://www.washingtonpost.com/travel/2020/04/08/travel-photographers-are-taking-epic-nature-photos-using-indoor-optical-illusions/
+
+[9] Jones, D. (2020, April 15) People miss flying so much they’re re-creating the airplane experience from home. Washington Post. Retrieved from https://www.washingtonpost.com/travel/2020/04/15/people-miss-flying-so-much-theyre-re-creating-airplane-experience-home/
+
+[10] Zhou, N. (2020, April 16) Coronavirus vacation: Australian family recreate 15-hour holiday flight in living room. The Guardian. https://www.theguardian.com/australia-news/2020/apr/16/coronavirus-vacation-australian-family-recreate-15-hour-holiday-flight-in-living-room
+
+[11] Güera, D., & Delp, E. J. (2018, November). [Deepfake video detection using recurrent neural networks](https://ieeexplore.ieee.org/document/8639163). In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE.
+
+[12] Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2008, June). [Lost in quantization: Improving particular object retrieval in large scale image databases](https://ieeexplore.ieee.org/document/4587635). In 2008 IEEE conference on computer vision and pattern recognition (pp. 1-8). IEEE.
+
+[13] UNWTO Tourism Highlights, 2017 Edition. (2017, August). Retrieved from http://www2.unwto.org/publication/unwto-tourism-highlights-2017
+
+[14] Tourism in Paris - Key Figures - Paris tourist office. Retrieved from https://press.parisinfo.com/key-figures/Tourism-in-Paris-Key-Figures
+
+#### Recommended Reading
+Lalonde, J. F., Hoiem, D., Efros, A. A., Rother, C., Winn, J., & Criminisi, A. (2007). [Photo clip art](https://dl.acm.org/doi/10.1145/1276377.1276381). ACM transactions on graphics (TOG), 26(3), 3.
+
+Lin, C. H., Yumer, E., Wang, O., Shechtman, E., & Lucey, S. (2018, March). [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_ST-GAN_Spatial_Transformer_CVPR_2018_paper.pdf). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9455-9464).
+
+For more insight on the state of the art in segmentation, you could take a look at the winner of COCO 2018. The slides of the winner’s presentation can be found here: http://presentations.cocodataset.org/ECCV18/COCO18-Detect-MMDET.pdf.
+Furthermore there are also industry solutions that offer segmentation, such as https://www.remove.bg and https://online.photoscissors.com.
+#### Task Organizers
+
+
+
+
+
+
+#### Task Schedule
+* 31 July: Data release
+* ~~31 October~~ 9 November: Runs due
+* 16 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/sportsvideo.md b/_editions/2020/tasks/sportsvideo.md
index db1b048c6..b1821e6c6 100644
--- a/_editions/2020/tasks/sportsvideo.md
+++ b/_editions/2020/tasks/sportsvideo.md
@@ -2,61 +2,68 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: Sports Video Classification
+subtitle: Classification of Strokes in Table Tennis games from videos
+blurb: Participants are provided with a set of videos of table tennis games and are required to build a classification system that automatically labels video segments with the strokes that players can be seen using in those segments. The ultimate goal of this research is to produce automatic annotation tools for sport faculties, local clubs and associations to help coaches to better assess and advise athletes during training.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+Participants are provided with a set of videos of table tennis games and are required to build a classification system that automatically labels video segments with the strokes that players can be seen using in those segments. The ultimate goal of this research is to produce automatic annotation tools for sport faculties, local clubs and associations to help coaches to better assess and advise athletes during training.
-#### Introduction
+Action detection and classification is one of the main challenges in visual content analysis and mining. Sport video analysis has been a very popular research topic, due to the variety of application areas, ranging from analysis of athletes' performances to multimedia intelligent devices with user-tailored digests. Datasets focused on sports activities or datasets including a large amount of sport activity classes are now available and many research contributions benchmark on those datasets. A large amount of work is also devoted to fine-grained classification through the analysis of sport gestures using motion capture systems. However, body-worn sensors and markers could disturb the natural behavior of sports players. Furthermore, motion capture devices are not always available for potential users, be it a University Faculty or a local sport team. Giving end-users the possibility to monitor their physical activities in ecological conditions through simple equipment is a challenging issue.
+This task offers researchers an opportunity to compare their approaches to fine-grained sports Video Annotation by testing them on the task of recognizing strokes in table tennis videos. The low inter-class variability makes the task more difficult than with usual general datasets, like UCF-101 and DeepMind Kinetics.
-#### New for 2020
-
-
-#### Target group
-
+#### Target Group
+The task is of interest to researchers in the areas of machine learning (classification), visual content analysis, computer vision and sport performance. We explicitly encourage researchers focusing specifically in domains of computer-aided analysis of sport performance.
#### Data
-
-
-#### Ground Truth
-
+Our focus is on recordings that have been made by widespread and cheap video cameras, e.g. GoPro. We use a dataset specifically recorded in a sport faculty facility and continuously completed by students and teachers. This dataset is constituted of player-centred videos recorded in natural conditions without markers or sensors. It comprises 20 table tennis strokes and a rejection class can be build upon them. The problem is hence a typical research topic in the field of video indexing: for a given recording, we need to label the video by recognizing each stroke appearing in the whole video.
#### Evaluation Methodology
-
+Twenty stroke classes are considered according to the rules of table tennis. This taxonomy was designed with professional table tennis teachers. We are working on videos recorded at the Faculty of Sports of the University of Bordeaux. Students are the sportsmen filmed and the teachers are supervising exercises conducted during the recording sessions. The recordings are markerless and allow the players to perform in natural conditions. In each video file a table tennis stroke is delimited by temporal borders. The latter are supplied in an xml file. For each test video the participants are invited to produce an xml file in which each stroke is labeled accordingly to a given taxonomy. Submissions will be evaluated in terms of accuracy per class of a stroke and of global accuracy.
#### References and recommended reading
+
+
+[Crisp Project](https://github.com/P-eMartin/crisp)
-### Big Picture of the Task
-
-#### Innovation
-
+Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. 2020. [Fine grained sport action recognition with siamese spatio-temporal convolutional neural networks.](https://link.springer.com/epdf/10.1007/s11042-020-08917-3) Multimedia Tools and Applications (19 Apr 2020).
-#### Focus
+Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, and Julien Morlier. 2019. [Optimal choice of motion estimation methods for fine-grained action classification with 3D convolutional networks.](https://hal.archives-ouvertes.fr/hal-02326240) In ICIP 2019. IEEE,554–558.
+Gül Varol, Ivan Laptev, and Cordelia Schmid. 2018. [Long-Term Temporal Convolutions for Action Recognition.](https://arxiv.org/pdf/1604.04494.pdf) IEEE Trans. Pattern Anal. Mach. Intell. 40, 6 (2018), 1510–1517.
-#### Risk management
+Joao Carreira and Andrew Zisserman. 2017. [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset.](https://arxiv.org/pdf/1705.07750.pdf) CoRR abs/1705.07750 (2017).
+Chunhui Gu, Chen Sun, Sudheendra Vijayanarasimhan, Caroline Pantofaru, David A. Ross, George Toderici, Yeqing Li, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, and Jitendra Malik. 2017. [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions.](http://openaccess.thecvf.com/content_cvpr_2018/papers/Gu_AVA_A_Video_CVPR_2018_paper.pdf) CoRR abs/1705.08421 (2017).
-#### Task organization team
+Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. [UCF101: A dataset of 101 hu- man actions classes from videos in the wild.](https://arxiv.org/pdf/1212.0402.pdf) CoRR 1212.0402 (2012).
+#### Task Organizers
+You can email us directly at mediaeval.sport.task (at) diff.u-bordeaux.fr
-#### Task organizers
-
-
-#### Task auxiliaries
+Jenny Benois-Pineau, Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400, Talence, France (jenny.benois-pineau (at) u-bordeaux.fr)
+Pierre-Etienne Martin, Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400, Talence, France (pierre-etienne.martin (at) u-bordeaux.fr)
+Renaud Péteri, MIA, University of La Rochelle, La Rochelle, France (renaud.peteri (at) univ-lr.fr)
+Boris Mansencal, Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400, Talence, France (boris.mansencal (at) labri.fr)
+Jordan Calandre, MIA, University of La Rochelle, La Rochelle, France
+Julien Morlier, IMS, University of Bordeaux, Talence, France
+Laurent Mascarilla, MIA, University of La Rochelle, La Rochelle, France
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* 31 October: Runs due
+* 15 November: Results returned
+* 30 November: Working notes paper
+* Early December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
diff --git a/_editions/2021.md b/_editions/2021.md
new file mode 100644
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--- /dev/null
+++ b/_editions/2021.md
@@ -0,0 +1,105 @@
+---
+layout: edition
+title: MediaEval 2021
+year: 2021
+permalink: /editions/2021/
+---
+
+The MediaEval Multimedia Evaluation benchmark offers tasks that are related to multimedia retrieval, analysis, and exploration. Participation is open to interested researchers who register. MediaEval focuses specifically on the human and social aspects of multimedia, and on multimedia systems that serve users. MediaEval tasks offer the opportunity for researchers to tackle challenges that bring together multiple modalities (visual, text, music, sensor data).
+
+
+
+### Workshop
+
+
+* Workshop schedule is here: [MediaEval 2021 Workshop Program](https://multimediaeval.github.io/editions/2021/docs/MediaEval2021WorkshopScheduleAndThanks.pdf)
+* Proceedings: [MediaEval 2021 Working Notes Proceedings](https://ceur-ws.org/Vol-3181/)
+* Video of Workshop Overview: [Opening presentation 13 Dec 2021](https://www.youtube.com/watch?v=h45gydsoM1M)
+
+Workshop group photo:
+
+
+
+
+
+
+
+### Important Dates (Updated)
+* July-September 2021: Data releases
+* Mid-November 2021: Runs due (See individual task pages for the exact deadlines)
+* 29 November 2021: Working notes paper due
+* 9 December 2021: Video and workshop-ready paper due
+* 13-15 December 2021: MediaEval 2021 Workshop Online (The workshop will be held during the "Golden Hours" 14:00-18:30 UTC+1)
+
+#### The MediaEval Organization
+MediaEval is made possible by the efforts of a larger number of task organizers, who each are responsible for organizing their own tasks. Please see the individual task pages for their name. The over all organization is carried out by the MediaEval Coorindation Committe and guided by the Community Council.
+
+##### The MediaEval Coordination Committee (2021)
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Steven Hicks, SimulaMet, Norway
+* Martha Larson, Radboud University, Netherlands (Overall coordinator and main contact person)
+
+##### Special Thanks to
+* Ngoc-Thanh Nguyen, University of Information Technology, Vietnam
+* Ricardo Manhães Savii, Dafiti Group, Brasil (Website)
+
+##### The MediaEval Community Council (2021)
+* Martha Larson, Radboud University, Netherlands (Coordinator and contact person)
+* Gareth J. F. Jones, Dublin City University, Dublin, Ireland
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+
+MediaEval is grateful for the support of [ACM Special Interest Group on Multimedia](http://sigmm.org/)
+
+
+
+For more information, contact m.larson (at) ru.cs.nl. You can also follow us on Twitter @multimediaeval
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+
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+This folder contains `Markdown` (.md) files to all tasks for 2021 MediaEval edition.
+
+## How to edit
+
+Opening a file and clicking on the pencil logo (view Figure 1 below)
+
+
+
+you will access `edit` mode on the file aand you will see something like below:
+
+
+
+There are 2 main parts to the document:
+
+* part 1 (lines 1 to 11) is the task file metadata. Here, you (task organizer), should fill in all `# required info` fields (title, subtitle, and blurb). When your task content is ready to be published on the website, to be shown on the website, then you should edit the `hide` property to `false`, this way your task will be visible on the website.
+
+* part 2 (lines 12 to infinity) is the actual task content. There is a suggested structure to the document to be followed. This part accepts content with [Markdown](https://daringfireball.net/projects/markdown/syntax) and HTML syntax.
+
+After you fill all content `commit changes` by filling the form below that edit screen and clicking on `Propose changes` as shown in Figure 3 below:
+
+
+
+That action will open a new window in which you will confirm a `Pull request`. As you can see in Figure 4 below:
+* yellow arrow points out where you can select a reviewer (if you are already talking to one of the website admins), this is optional
+* fill your comments on the `fill here` space as you believe it's required to support approval of your change.
+* red arrow points to the button that confirms your `Pull request`
+
+
+
+Other than that please feel free to ask for help. This structure is and experiment and we need help to turn it useful and easy to everyone. MediaEval organizers are available to help or submit questions and issues [here](https://github.com/multimediaeval/multimediaeval.github.io/issues).
+
diff --git a/_editions/2021/tasks/emergingnews.md b/_editions/2021/tasks/emergingnews.md
new file mode 100644
index 000000000..437643ec0
--- /dev/null
+++ b/_editions/2021/tasks/emergingnews.md
@@ -0,0 +1,68 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Emerging News: Detecting emerging stories from social media and news feeds"
+subtitle:
+blurb: "Emerging News task aims to explore novel ways to detect emerging stories from semantic streams of social media messages and news feeds."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+For news organisations it is critical to identify emerging stories as soon as they appear, delays can cause economic and audience losses. Keeping up-to-date journalists and readers is a highly demanding task. News agencies spend a lot of time and human power on continuously monitoring social media, TV, radio and blogs looking for those emerging news. Artificial Intelligence and Big Data technologies can assist news agencies and alleviate journalists on this tedious task by distilling the different media channels and keeping journalists in the loop for judging the newsworthiness of those identified emerging stories.
+
+Emerging News task aims to explore novel ways to detect emerging stories from semantic streams of social media messages and news feeds. Participants are expected to develop a real-time solution that identifies emerging stories. This solution must read from a stream of items and output those stories that could be considered emerging stories. Items are represented semantically using RDF and contain annotations of their named entities. The expected solutions must use the semantic representations to identify the potential emerging stories.
+
+#### Motivation and background
+Journalism is under pressure from loss of advertisement and revenues, in combination with competing online distribution channels that stream free content while experiencing an increase in digital consumption and readers who demand quality journalism and trusted sources [1]. Information is no longer consumed from a single newspaper. Instead, readers have access to and can contrast fresh and first-hand information sources available on the internet and social media at any time.
+
+Newsrooms compete between them in a demanding race to be the first ones to publish news about events and fresh stories [1]. The vast amount of information that is continuously being published on the internet makes it significantly challenging for journalists to distill daily events [2]. For example, Twitter publishes more than 500 million tweets a day (i.e., an average of 5700 tweets per second) [3] and more than 10000 English news articles are published online every day worldwide [4]. Some news agencies have digitalized their newsrooms processes and employ software solutions to support journalist work [5]. Automating the detection of emerging news from social media and news feeds can help news agencies to discover new stories when they are not the first ones to arrive or do not have enough resources (e.g., local news agencies).
+
+The task is proposed in context with the News Angler project [6]. The News Angler is a project that uses new information and communication technologies to leverage big data and social media. The project’s purpose is to support journalists in finding new and unexpected angles on unfolding news stories, along with suitable background information. The project, therefore, explores how artificial intelligence (AI) techniques can leverage big open data sources to support high-quality journalism. Central AI techniques so far are knowledge graphs and natural-language processing. Knowledge graphs and semantic technologies offer a standard form for representing information and knowledge. In this way, the collected information can be analysed, retrieved, and shared more easily and precisely in new ways.
+
+As part of the News Angler project, we developed an evolving platform that harvests potentially news-related information in real-time from textual sources, such as social media, commercial news aggregators, and open reference sources. We want to extend the platform with new components for analysing news items representation and providing newsworthy information to journalists. Eventually, participants of the EMERGING NEWS task may get access to the platform and we will consider further collaborations.
+
+#### Target group
+This task is of interest to researchers that work in domains like information and news retrieval, knowledge graphs, semantic technologies, natural-language processing and that are interested in creating a better environment and tools for journalists.
+
+#### Data
+The data is delivered through an API (participants need to fill a task-specific Usage Agreement to get access). The API provides a stream of JSON-LD with RDF graphs serialized in TURTLE. The RDF graphs annotate the social media and news items with the entities found in the text. The text is provided to help participants to understand the RDF annotations, it is not meant to be used for identifying emerging events, but it can be used for evaluation purposes.
+
+An example of the RDF graphs: http://newshunter.uib.no:5555/example (Text has been omitted)
+
+
+#### Evaluation methodology
+An expert panel evaluates the results based on their relevance. The expert panel will be formed by experts with relevant background in journalism and media. The experts will use the developed solution and judge if the information provided can be considered as an emerging story or not and how useful the information is. During the evaluation, all participants will use the same data set. Experts do not know RDF, therefore they will base their decisions on the textual information provided on the RDF representations.
+
+### Task requirements
+* Input: stream of JSON-LD data (it can be either a continuous stream or time windows batches of. for example, 5, 10, 15, 20 min. We expect participants to choose the most suitable set up for their solution and discuss it).
+* Output: a group of JSON-LD items that belongs to the emerging story or a single JSON-LD item that is an emerging story (we leave it to the participants' decision too)
+* Visualization: a User Interface is optional
+* Expected delivery: Dockerized API and instructions on how to run it. Optionally it can be accompanied by another python code or docker that simulates the data input/interaction.
+* Language: Python
+* License: MIT License
+
+#### References and recommended reading
+[1] Jorge Vázquez-Herrero, Sabela Direito-Rebollal, Alba Silva-Rodríguez and Xosé López-García. 2020. [Journalistic Metamorphosis: Media Transformation in the Digital Age](https://doi.org/10.1007/978-3-030-36315-4). Springer International Publishing.\
+[2] Ulrich Germann, Renārs Liepins, Guntis Barzdins, Didzis Gosko, Sebastião Miranda and David Nogueira. 2018. [The SUMMA platform: A scalable infrastructure for multi-lingual multimedia monitoring](https://doi.org/10.18653/v1/P18-4017). System Demonstrations, Proceedings of ACL 2018.\
+[3] Raffi Krikorian. 2013. [New tweets per second record, and how!](https://blog.twitter.com/engineering/en_us/a/2013/new-tweets-per-second-record-and-how.html). Twitter Blog.\
+[4] Felix Hamborg, Norman Meuschke and Bela Gipp. 2020. [Bias-aware news analysis using matrix-based news aggregation](https://doi.org/10.1007/s00799-018-0239-9). International Journal on Digital Libraries.\
+[5] Marc Gallofré Ocaña and Andreas Lothe Opdahl. 2020. [Challenges and opportunities for journalistic knowledge platforms](http://ceur-ws.org/Vol-2699/paper43.pdf). Proceedings of the CIKM 2020 Workshops. Galway, Ire-land (2020)\
+[6] Marc Gallofré Ocaña, Lars Nyre, Andreas Lothe Opdahl, Bjørnar Tessem, Christoph Trattner, Csaba Veres. 2018. [Towards a big data platform for news angles](http://ceur-ws.org/Vol-2316/paper1.pdf). The 4th Norwegian Big Data Symposium (NOBIDS).
+
+#### Task organizers
+* Marc Gallofré Ocaña, University of Bergen, Norway
+* Andreas L. Opdahl, University of Bergen, Norway
+* Duc-Tien Dang-Nguyen, University of Bergen, Norway
+
+#### Task Schedule (Updated)
+* 16 August: Data is made available
+* 15 November: Runs due
+* 22 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December 2021: MediaEval 2021 Workshop Online
diff --git a/_editions/2021/tasks/emotionalmario.md b/_editions/2021/tasks/emotionalmario.md
new file mode 100644
index 000000000..e765b19f6
--- /dev/null
+++ b/_editions/2021/tasks/emotionalmario.md
@@ -0,0 +1,76 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Emotional Mario: A Games Analytics Challenge"
+subtitle:
+blurb: "Carry out analysis of emotion on videos and biometric data of players to predict key events in the gameplay. Optionally, use these predictions to create a highlights video containing the best moments of gameplay."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+In this task, participants carry out multimedia analysis in order to gain insight into the emotion of players playing video games. The task is called *Emotional Mario* because it focuses on the iconic video game Super Mario Bros.
+
+***Subtask 1: Event Detection:*** Participants carry out emotional anlaysis on facial videos and biometric data (e.g., heart rate and skin conductivity) collected from players playing the game. The goal is to identify key events (i.e., events of high significance in the gameplay). Such key events include *the end of a level*, a *power-up* or *extra life for Mario*, or *Mario’s death*.
+
+***Subtask 2: Gameplay Summarization (Optional):*** Participants create a video summary of the best moments of the play. This can include gameplay scenes, facial video, data visualization, and whatever comes to your mind that you find important to tell the *game story*, the story of what happened during the game.
+
+
+
+Subtask 1 is a technical task, and easily approachable. Subtask 2 is an open, creative task, which builds on event detection. Past experience has shown that it is easier to step into the task with a technical subtask, and use that as inspiration for the creative task. The ultimate goal of the Emotional Mario task is to develop new approaches to the creative task of telling the story of a game.
+*Participants are encouraged to make their code public with their submission.*
+
+
+#### Motivation and background
+As games are designed to evoke emotions [1], we hypothesize that emotions in the player are reflected in the visuals of the video game. Simple examples are when players are happy after having mastered a particularly complicated challenge, when they are shocked by a jump scare scene in a horror game, or when they are excited after unlocking a new resource. These things can be measured by questionnaires after playing [2], but in the Emotional Mario task, we want to interconnect emotions and gameplay based on data instead of asking the players.
+
+With the rise of deep learning, many large leaps in research have been achieved in recent years such as human-level image recognition, text classification, and even content creation. Games and deep learning also have a rather long history together, specifically in the context of reinforcement learning. However, video games still pose a lot of challenges. Games are understood as engines of experience [1], and as such, they need to invoke human emotions. While emotion recognition has come a far way over the last decade [3], the connection between emotions and video games is still an open and interesting research question. In the Emotional Mario task, we aim to leverage deep learning to move forward our understanding of the role of emotions in video games
+
+#### Target group
+The target group for this task is diverse and broad. It includes researchers and practitioners from game design and development, game studies, machine learning, artificial intelligence, and interactive multimedia. We also encourage interdisciplinary research involving people from psychology, game studies, and the humanities discussing the interrelation of biometric data, facial expressions, and gameplay. In any case, regardless of the research background, the submission will help to have a basic understanding of how we can better understand the connection between gameplay and the reaction of the player.
+
+#### Data
+For the EmotionalMario challenge, we focus on the iconic Super Mario Bros. video game and provide a multimodal data set based on a Super Mario Bros. implementation for OpenAI Gym. The data set, called Toadstool [4], contains for a population of ten players their game input, demographics, biomedical sensory input from a medical-grade device (e.g., heart rate and skin conductivity) as well as videos of their faces while playing the game. The data set also contains he gameplay itself, demographics on the players and their scores and times spent in the game.he gameplay itself, demographics on the players and their scores and times spent in the game.
+
+Additionally, we provide ground truth for special events within the gameplay for eight of the study participants. We extracted the data from the gameplay session file. The remaining two serve as data for the runs to be submitted.
+
+The toadstool data is available from [https://osf.io/qrkcf/](https://osf.io/qrkcf/) and the ground truth can be downloaded from [https://www.itec.aau.at/~mlux/files/EmotionalMario2021_Training_Data.zip](https://www.itec.aau.at/~mlux/files/EmotionalMario2021_Training_Data.zip). For a head start we've done an analysis with [FER](https://github.com/justinshenk/fer) of the facial videos. You can find the result [here](https://drive.google.com/drive/folders/1L2kYwcmYDFuALLGdI5vHgOLESMCS6EJ_?usp=sharing).
+
+#### Evaluation methodology
+
+***Subtask 1: Event Detection:*** In this subtask, participants identify events in the gameplay by making use of biometric data and videos of facial expressions. These events include player deaths, obtaining power-ups, and completing a level. Ground truth as well as an evaluation script will be provided.
+We also encourage participants to carry out a failure analysis of their results in order to gain insight in the mistakes that their classifiers make.
+
+***Subtask 2: Gameplay Summarization:*** In this subtask, participants are asked to submit a summary video for a study participant from the Toadstool data. There is no constraint on the modalities of the story, so it can be video, audio, text, images, or a combination thereof. An expert panel with professionals and researchers from the field of game development, game studies, e-sports, and media sciences will then investigate the submissions and judge them for:
+* Informative value (i.e. is it a good summary of the gameplay),
+* Accuracy (i.e. does it reflect the emotional up and downs and the skill of the play), and
+* Innovation (ie. surprisingly new approach, non-linearity of the story, creative use of cuts, etc.)
+
+#### References and recommended reading
+
+[1] Tynan Sylvester. 2013. [Designing games: A guide to engineering experiences](https://www.oreilly.com/library/view/designing-games/9781449338015/). O'Reilly Media, Inc.
+
+[2] Abeele, V. V., Spiel, K., Nacke, L., Johnson, D., and Gerling, K. 2020. [Development and validation of the player experience inventory: A scale to measure player experiences at the level of functional and psychosocial consequences](https://lirias.kuleuven.be/retrieve/580761). International Journal of Human-Computer Studies, 135, 102370.
+
+[3] Saxena, Anvita, Ashish Khanna, and Deepak Gupta. 2020. [Emotion recognition and detection methods: A comprehensive survey.](https://iecscience.org/uploads/jpapers/202003/dnQToaqdF8IRjhE62pfIovCkDJ2jXAcZdK6KHRzM.pdf) Journal of Artificial Intelligence and Systems 2.1 (2020): 53-79.
+
+[4] Henrik Svoren, Vajira Thambawita, Pål Halvorsen, Petter Jakobsen, Enrique Garcia-Ceja, Farzan Majeed Noori, Hugo L. Hammer, Mathias Lux, Michael Alexander Riegler, and Steven Alexander Hicks. 2020. [Toadstool: A Dataset for Training Emotional Intelligent Machines Playing Super Mario Bros.](https://dl.acm.org/doi/abs/10.1145/3339825.3394939) In Proceedings of the 11th ACM Multimedia Systems Conference (MMSys ’20). Association for Computing Machinery, New York, NY, USA, 309–314.
+
+
+#### Task organizers
+* Mathias Lux (Alpen-Adria-Universität Klagenfurt, AT; mathias.lux@aau.at)
+* Michael Riegler, Pål Halvorsen, Vajira Thambawita, and Steven Hicks (SimulaMet Oslo, NO)
+* Duc-Tien Dang-Nguyen and Kristine Jorgensen (University of Bergen, NO)
+
+#### Task Schedule (Updated)
+* 13 July: Data release
+* 14 November: Runs due
+* 22 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December 2021: MediaEval 2021 Workshop Online
+
diff --git a/_editions/2021/tasks/fakenews.md b/_editions/2021/tasks/fakenews.md
new file mode 100644
index 000000000..cdeaa6d3e
--- /dev/null
+++ b/_editions/2021/tasks/fakenews.md
@@ -0,0 +1,107 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task"
+subtitle: "Fighting against misinformation spreading"
+blurb: "The FakeNews task explores various machine-learning approaches to automatically detect misinformation and its spreaders in social networks."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+
+The FakeNews Detection Task offers three fake news detection subtasks on COVID-19-related conspiracy theories. The first subtask includes text-based fake news detection, the second subtask targets the detection of conspiracy theory topics, and the third subtask combines topic and conspiracy detection. All subtasks are related to misinformation disseminated in the context of the long-lasting COVID-19 crisis. We focus on conspiracy theories that assume some kind of nefarious actions by governments or other actors related to CODID-19, such as intentionally spreading the pandemic, lying about the nature of the pandemic, or using vaccines that have some hidden functionality and purpose.
+
+***Text-Based Misinformation Detection***: In this subtask, the participants receive a dataset consisting of tweet text blocks in English related to COVID-19 and various conspiracy theories. **The participants are encouraged to build a multi-class classifier that can flag whether a tweet promotes/supports or discusses at least one (or many) of the conspiracy theories**. In the case if the particular tweet promotes/supports one conspiracy theory and just discusses another, the result of the detection for the particular tweet is experted to be equal to "**stronger**" class: promote/support in the given sample.
+
+***Text-Based Conspiracy Theories Recognition***: In this subtask, the participants receive a dataset consisting of tweet text blocks in English related to COVID-19 and various conspiracy theories. **The main goal of this subtask is to build a detector that can detect whether a text in any form mentions or refers to any of the predefined conspiracy topics**.
+
+***Text-Based Combined Misinformation and Conspiracies Detection***: In this subtask, the participants receive a dataset consisting of tweet text blocks in English related to COVID-19 and various conspiracy theories. **The goal of this subtask is to build a complex multi-labelling multi-class detector that for each topic from a list of predefined conspiracy topics can predict whether a tweet promotes/supports or just discusses that particular topic**.
+
+
+
+#### Motivation and background
+
+Digital wildfires, i.e., fast-spreading inaccurate, counterfactual, or intentionally misleading information, can quickly permeate public consciousness and have severe real-world implications, and they are among the top global risks in the 21st century. While a sheer endless amount of misinformation exists on the internet, only a small fraction of it spreads far and affects people to a degree where they commit harmful and/or criminal acts in the real world. The COVID-19 pandemic has severely affected people worldwide, and consequently, it has dominated world news for months. Thus, it is no surprise that it has also been the topic of a massive amount of misinformation, which was most likely amplified by the fact that many details about the virus were unknown at the start of the pandemic. This task aims at the development of methods capable of detecting such misinformation. Since many different misinformation narratives exist, such methods must be capable of distinguishing between them. For that reason we consider a variety of well-known conspiracy theories related to COVID-19.
+
+
+#### Target group
+
+The task is of interest to researchers in the areas of online news, social media, multimedia analysis, multimedia information retrieval, natural language processing, and meaning understanding and situational awareness to participate in the challenge.
+
+
+#### Data
+
+The dataset contains several sets of tweet texts mentioning Corona Virus and different conspiracy theories. The dataset set consists of only English language posts and it contains a variety of long tweets with neutral, positive, negative, and sarcastic phrasing. The datasets is ***not balanced*** with respect to the number of samples of conspiracy-promoting and other tweets, and the number of tweets per each conspiracy class. The dataset items have been collected from Twitter during a period between 20th of January 2020 and 31st of July 2021, by searching for the Corona-virus-related keywords (e.g., "corona", "COVID-19", etc.) inside the tweets' text, followed by a search for keywords related to the conspiracy theories. Since not all tweets are available online, the partipants will be provided a full-text set of already downloaded tweets. In order to be compliant with the Twitter Developer Policy, only the members of the participants' participating temas are allowed to access and use the provided dataset. Distribution, publication, sharing and any form of usage of the provided data apart of the research purposes within the FakeNews task is strictly prohibited. A copy of the dataset in form of Tweet ID and annotations will be published after the end of MediaEval 2021.
+
+
+#### Ground truth
+
+The ground truth for the provided dataset was created by the team of well-motivated students and researchers using overlapping annotation process with the following cross-validation and verification by an independent assisting team.
+
+
+#### Evaluation methodology
+
+Evaluation will be performed using standard implementation of the multi-class generalization of the Matthews correlation coefficient (MCC, https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html) computed on the optimally threshold conspiracy promoting probabilities (threshold that yields the best MCC score).
+
+#### References and recommended reading
+
+
+
+***General***
+
+[1] Nyhan, Brendan, and Jason Reifler. 2015. [Displacing misinformation about events: An experimental test of causal corrections](https://www.cambridge.org/core/journals/journal-of-experimental-political-science/article/displacing-misinformation-about-events-an-experimental-test-of-causal-corrections/69550AB61F4E3F7C2CD03532FC740D05#). Journal of Experimental Political Science 2, no. 1, 81-93.
+
+***Twitter data collection and analysis***
+
+[2] Burchard, Luk, Daniel Thilo Schroeder, Konstantin Pogorelov, Soeren Becker, Emily Dietrich, Petra Filkukova, and Johannes Langguth. 2020. [A Scalable System for Bundling Online Social Network Mining Research](https://ieeexplore.ieee.org/document/9336577). In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 1-6.
+
+[3] Schroeder, Daniel Thilo, Konstantin Pogorelov, and Johannes Langguth. 2019. [FACT: a Framework for Analysis and Capture of Twitter Graphs](https://ieeexplore.ieee.org/document/8931870). In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 134-141.
+
+[4] Achrekar, Harshavardhan, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, and Benyuan Liu. 2011. [Predicting flu trends using twitter data](https://ieeexplore.ieee.org/document/5928903). In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, 702-707.
+
+[5] Chen, Emily, Kristina Lerman, and Emilio Ferrara. 2020. [Covid-19: The first public coronavirus twitter dataset](https://arxiv.org/abs/2003.07372v1?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+CoronavirusArXiv+%28Coronavirus+Research+at+ArXiv%29). arXiv preprint arXiv:2003.07372.
+
+[6] Kouzy, Ramez, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. [Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152572/). Cureus 12, no. 3.
+
+***Natural language processing***
+
+[7] Bourgonje, Peter, Julian Moreno Schneider, and Georg Rehm. 2017. [From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles](https://www.aclweb.org/anthology/W17-4215/). In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, 84-89.
+
+[8] Imran, Muhammad, Prasenjit Mitra, and Carlos Castillo. 2016. [Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages](https://arxiv.org/abs/1605.05894). arXiv preprint arXiv:1605.05894.
+
+***Information spreading***
+
+[9] Liu, Chuang, Xiu-Xiu Zhan, Zi-Ke Zhang, Gui-Quan Sun, and Pak Ming Hui. 2015. [How events determine spreading patterns: information transmission via internal and external influences on social networks](https://iopscience.iop.org/article/10.1088/1367-2630/17/11/113045/pdf). New Journal of Physics 17, no. 11.
+
+***Online news sources analysis***
+
+[10] Pogorelov, Konstantin, Daniel Thilo Schroeder, Petra Filkukova, and Johannes Langguth. 2020. [A System for High Performance Mining on GDELT Data](https://ieeexplore.ieee.org/document/9150419). In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE, 1101-1111.
+
+
+#### Task organizers
+* Konstantin Pogorelov, Simula Research laboratory (Simula), Norway, konstantin (at) simula.no
+* Johannes Langguth, Simula Research laboratory (Simula), Norway, langguth (at) simula.no
+* Daniel Thilo Schroeder, Simula Research laboratory (Simula), Norway
+
+
+#### Task auxiliaries
+* Özlem Özgöbek, Norwegian University of Science and Technology (NTNU), Norway
+
+#### Task Schedule (Updated)
+* 25 August: Initial development set release
+* 21 October: Full development set release
+* 18 November: Final test set release
+* 24 November: Runs due
+* 25 November: Results returned
+* 29 November: Working notes paper due
+* 13 December - 15 December, 14:00-18:30 CET (UTC+1): MediaEval 2021 Workshop
+
+
+#### Acknowledgments
+This work was funded by the Norwegian Research Council under contracts #272019 and #303404 and has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract #270053. We also acknowledge support from Michael Kreil in the collection of Twitter data.
diff --git a/_editions/2021/tasks/medico.md b/_editions/2021/tasks/medico.md
new file mode 100644
index 000000000..f103026f1
--- /dev/null
+++ b/_editions/2021/tasks/medico.md
@@ -0,0 +1,80 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Medico: Transparency in Medical Image Segmentation"
+subtitle:
+blurb: "The Medico task explores the use of transparent approaches to automatically segment images collected from the human colon."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+The fight against colorectal cancer requires better diagnosis tools. Computer-aided diagnosis systems can reduce the chance that diagnosticians overlook a polyp during a colonoscopy. As machine learning becomes more common, even in high-risk fields like medicine, the need for transparent systems becomes more critical. In this case, transparency is defined as giving as much detail as possible on the different parts that make up a machine learning pipeline, including everything from data collection to final prediction. This task focuses on robust, transparent, and efficient algorithms for polyp segmentation.
+
+The data consists of a large number of endoscopic images of the colon, which have been labeled by expert gastroenterologists.
+
+*Subtask 1: Polyp Segmentation:* The polyp segmentation task asks participants to develop algorithms for segmenting polyps in images taken from endoscopies. The main focus of this task is to achieve high segmentation metrics on the supplied test dataset. Since [Medico 2020](https://multimediaeval.github.io/editions/2020/tasks/medico/), we have extended the development dataset and created a new testing dataset to which the submissions will be evaluated on.
+
+*Subtask 2: Algorithm Efficiency* The algorithm efficiency task is similar to subtask one, but puts a stronger emphasis on the algorithm's speed in terms of frames-per-second. To ensure a fair evaluation, this task requires participants to submit a Docker image so that all algorithms are evaluated on the same hardware.
+
+
+*Subtask 3: Transparent Machine Learning Systems* The transparency task tries to measure the transparency of the systems used for the aforementioned segmentation tasks. The main focus for this task is to evaluate systems from a transparency point of view, meaning for example explanations of how the model was trained, the data that was used, and interpretation of a model's predictions.
+
+Participants are encouraged to make their code public with their submission.
+
+#### Motivation and background
+Medical image segmentation is a topic that has garnered a lot of attention over the last few years. Compared to classification and object detection, segmentation gives a more precise region of interest for a given class. This is immensely useful for the doctors as it not only specifies that an image contains something interesting but also where to look at which also provides some kind of inherent explanation. Colonoscopies are a perfect use-case for medical image segmentation as they contain a great variety of different findings that may be easily overlooked during the procedure. Furthermore, transparent and interpretable machine learning systems are important to explain the *whys* and the *hows* of the predictions. This is especially important in medicine, where conclusions based on wrong decisions resulted from either biased or incorrect data, faulty evaluation or simply a bad model could be fatal. For this reason, the *Medico: Transparency in Medical Image Segmentation* task aims to develop automatic segmentation systems that are transparent and explainable.
+
+#### Target group
+The task is of interest to the researchers working with multimedia segmentation, deep learning (semantic segmentation), and computer vision. We especially encourage young researchers to contribute to the field of endoscopy by developing an automated computer-aided diagnosis system that could be potentially used in clinical settings.
+
+#### Data
+*Subtask 1: Polyp Segmentation:* We will use a slightly modified version of the segmentation part of HyperKvasir [1] that will include additional polyps for training and a separate testing dataset.
+
+*Subtask 2: Algorithm Efficiency* Same as subtask 1.
+
+*Subtask 3: Transparent Machine Learning Systems* The transparent machine learning system task will be based on the previous two tasks and will use each respective dataset.
+
+#### Ground truth
+The ground truth for the provided dataset was created by an experienced computer scientist and medical doctor, which was then verified by an expert gastroenterologist with over ten years of experience.
+
+#### Evaluation methodology
+*Subtask 1: Polyp Segmentation* We will use the standard metrics commonly used to evaluate segmentation tasks, similar to what was presented in [Medico 2020](https://multimediaeval.github.io/editions/2020/tasks/medico/). This includes the Dice coefficient, pixel accuracy, and the Intersection-Over-Union (Jaccard index). The metric which will be used to rank submissions will be the Intersection-Over-Union coefficient.
+
+*Subtask 2: Algorithm Efficiency* For the Algorithm Efficiency Task, we require participants to submit their detection algorithm as part of a Docker image so that we can evaluate it on our hardware. Submissions for this task will be evaluated based on the algorithms speed and segmentation performance. Speed will be measured by frames-per-second, while segmentation performance will be measured using the same metrics as described in Task 1.
+
+*Subtask 3: Transparent Machine Learning Systems:* We perform a qualitative evaluation of the submission. Here, a multi-disciplinary team will evaluate the submissions based on how transparent and understandable they are. We encourage participants to perform failure analysis on their results, which will contribute to more insight into where a model makes mistakes, contributing to the overall transparency of the system.
+
+#### References and recommended reading
+
+
+
+[1] [Borgli, H., Thambawita, V., Smedsrud, P.H. et al. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data 7, 283 (2020).](https://www.nature.com/articles/s41597-020-00622-y)
+
+[2] [Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In Proceeding of International Conference on Medical image computing and computer-assisted intervention (MICCAI), 234-241, 2015.](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28)
+
+[3] [Weller, A. (2019). Transparency: Motivations and Challenges. In W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, & K.-R. Müller (Eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 23–40). Springer International Publishing.](https://doi.org/10.1007/978-3-030-28954-6_2)
+
+[4] [Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 2019 (pp. 23-40). Springer, Cham.](https://link.springer.com/book/10.1007/978-3-030-28954-6)
+
+#### Task organizers
+* Steven Hicks, SimulaMet, Norway steven (at) simula.no
+* Debesh Jha, SimulaMet, Norway debesh (at) simula.no
+* Vajira Thambawita, SimulaMet and OsloMet, Norway
+* Hugo Hammer, OsloMet, Norway
+* Thomas de Lange, Bærum Hospital, Norway
+* Sravanthi Parasa, Swedish Medical Center, Sweden
+* Michael Riegler, SimulaMet, Norway
+* Pål Halvorsen, SimulaMet and OsloMet, Norway
+
+#### Task Schedule (Updated)
+* 1 July: Data release
+* 16 November: Runs due
+* 22 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December 2021: MediaEval 2021 Workshop Online
diff --git a/_editions/2021/tasks/memorability.md b/_editions/2021/tasks/memorability.md
new file mode 100644
index 000000000..2d08814d2
--- /dev/null
+++ b/_editions/2021/tasks/memorability.md
@@ -0,0 +1,90 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Predicting Media Memorability"
+subtitle:
+blurb: "The task requires participants to automatically predict memorability scores for videos, that reflect the probability for a video to be remembered. Participants will be provided with an extensive data set of videos with memorability annotations, related information, and pre-extracted state-of-the-art visual features."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+Understanding what makes a video memorable has a very broad range of current applications, e.g., education and learning, content retrieval and search, content summarization, storytelling, targeted advertising, content recommendation and filtering. This task requires participants to automatically predict memorability scores for videos that reflect the probability for a video to be remembered over both a short and long term. Participants will be provided with an extensive data set of videos with memorability annotations, related information, and pre-extracted state-of-the-art visual features.
+
+*Subtask 1: Video-based prediction:*
+Participants are required to generate automatic systems that predict short-term and long-term memorability scores of new videos based on the given video dataset and their memorability scores.
+
+
+*Subtask 2: Generalization (optional):*
+Participants will train their system on one of the two sources of data we provide and will test them on the other source of data. This is an optional subtask.
+
+*Subtask 3: EEG-based prediction (pilot):*
+Participants are required to generate automatic systems that predict short-term memorability scores of new videos based on the given EEG data. This is a pilot subtask.
+
+
+#### Motivation and background
+Enhancing the relevance of multimedia occurrences in our everyday life requires new ways to organize – in particular, to retrieve – digital content. Like other aspects of video importance, such as aesthetics or interestingness, memorability can be regarded as useful to help make a choice between competing videos. This is even truer when one considers the specific use cases of creating commercials or creating educational content.
+
+Efficient memorability prediction models will also push forward the semantic understanding of multimedia content, by putting human cognition and perception in the center of the scene understanding. Because the impact of different multimedia content, images or videos, on human memory is unequal, the capability of predicting the memorability level of a given piece of content is obviously of high importance for professionals in the fields of advertising, filmmaking, education, content retrieval, etc., which may also be impacted by the proposed task.
+
+#### Target group
+Researchers will find this task interesting if they work in the areas of human perception and scene understanding, such as image and video interestingness, memorability, attractiveness, aesthetics prediction, event detection, multimedia affect and perceptual analysis, multimedia content analysis, machine learning (though not limited to).
+
+#### Data
+
+In 2021, the task will use a subset of TRECVID 2019 Video-to-Text video dataset similar to the previous year. This year, more annotations will be provided to improve the quality of the collection. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task, memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorisation of the videos (short term), and then 24 to 72 hours later (long term). The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Histograms in the HSV and RGB spaces, HOG, LBP, and deep features extracted from AlexNet, VGG and C3D. In comparison to the videos used for this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view.
+
+Additionally, we will open the Memento10k [8] dataset to participants. This dataset contains 10.000 three-second videos depicting in-the-wild scenes, with their associated short term memorability scores, memorability decay values, action labels, and 5 accompanying captions. 7000 videos will be released as a training set, and 1500 will be given for validation. The last 1500 videos will be used as the test set for scoring submissions. The scores are computed with 90 annotations per video on average, and the videos were deafened before being shown to participants. We will also distribute a set of features for each video analogous to the Trecvid set.
+
+Apart from traditional video information like metadata and extracted visual features, part of the data will be accompanied by Electroencephalography (EEG) recordings that would allow to explore the physical reaction of the user. Optionally, we may use descriptive captions from their use in the TRECVid automatic video captioning task.
+
+*Subtask 1: Video-based prediction:* Data is a subset of a collection consisting of 1,500 short videos retrieved from TRECVid. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task [6], memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorization of the videos (short term), and then 24 to 72 hours later (long term). In 2021, the same training and test sets as in 2020 will be used including 590 videos as part of the training set and 410 additional videos as part of the development set. More annotations will be collected to improve the quality of the collection. The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Aesthetic Features, C3D, Captions, Colour Histograms, HMP, HoG, Fc7 layer from InceptionV3, LBP, or ORP. In comparison to the videos used in this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view. Additionally, we will also use data from the Memento [8] dataset, that contains short-term memorability annotations, while distributing a similar set of features.
+
+*Subtask 2: Generalization (optional):* The aim of the Generalization subtask is to check system performance on other types of video data. Participants will use their systems, trained on one of the two sources of data we propose, to predict the memorability of videos from the testing set of the other source of data. We believe this would provide interesting insights into the performance of the developed systems, given that, while the two sources of data measure memorability in a similar way, the videos may be somewhat different with regards to their content, general subjects or length. As this will be an optional task, participants are not required to participate in it.
+
+*Subtask 3: EEG-based prediction (pilot):* The aim of the Memorability-EEG pilot subtask is to promote interest in the use of neural signals—either alone, or in combination with other data sources—in the context of predicting video memorability by demonstrating what EEG data can provide. The dataset will be a subset of videos from subtask 1 for which EEG data has been gathered, and pre-extracted features will be used. This demonstration pilot will enable interested researchers to see how they could use neural signals without any of the requisite domain knowledge in a future Memorability task, potentially increasing interdisciplinary interest in the subject of memorability, and opening the door to novel EEG-computer vision combined approaches to predicting video memorability. Pre-selected participants in this pilot demonstration will use the dataset to explore all manners of machine learning and processing strategies to predict video memorability. This will lead to a short paper and presentation on their findings, which will ultimately contribute towards the collaborative definition of a fully-fledged task at MediaEval 2022, where participating teams will submit runs and be benchmarked.
+
+#### Ground truth
+The ground truth for memorability will be collected through recognition tests, and thus results from objective measures of memory performance.
+
+#### Evaluation methodology
+The outputs of the prediction models – i.e., the predicted memorability scores for the videos – will be compared with ground truth memorability scores using classic evaluation metrics (e.g., Spearman’s rank correlation).
+
+#### References and recommended reading
+
+
+[1] Aditya Khosla, Akhil S Raju, Antonio Torralba, and Aude Oliva. 2015. [Understanding and predicting image memorability at a large scale](https://people.csail.mit.edu/khosla/papers/iccv2015_khosla.pdf), In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2390–2398.\
+[2] Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva. 2014. [What makes a photograph memorable?](http://web.mit.edu/phillipi/www/publications/memory_pami.pdf) IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 7 (2014), 1469–1482.\
+[3] Hammad Squalli-Houssaini, Ngoc Duong, Gwenaëlle Marquant, and Claire-Hélène Demarty. 2018. [Deep learning for predicting image memorability](https://hal.archives-ouvertes.fr/hal-01629297/file/main.pdf), In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2371-2375.\
+[4] Junwei Han, Changyuan Chen, Ling Shao, Xintao Hu, Jungong Han, and Tianming Liu. 2015. [Learning computational models of video memorability from fMRI brain imaging](https://ieeexplore.ieee.org/abstract/document/6919270). IEEE Transactions on Cybernetics 45, 8 (2015), 1692–1703.\
+[5] Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, and Akhil Shetty. 2017. [Show and Recall: Learning What Makes Videos Memorable](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w40/Shekhar_Show_and_Recall_ICCV_2017_paper.pdf). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2730–2739.\
+[6] Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. 2019. [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://openaccess.thecvf.com/content_ICCV_2019/papers/Cohendet_VideoMem_Constructing_Analyzing_Predicting_Short-Term_and_Long-Term_Video_Memorability_ICCV_2019_paper.pdf). In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2531-2540. \
+[7] Mihai Gabriel Constantin, Miriam Redi, Gloria Zen, and Bodgan Ionescu. 2019. [Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates](http://campus.pub.ro/lab7/bionescu/index_files/pub/2018_ACM_CSUR-draft.pdf). ACM Computing Surveys, 52(2). \
+[8] Anelise Newman, Camilo Fosco, Vincent Casser, Allen Lee, Barry McNamara, and Aude Oliva. 2020. [Modeling Effects of Semantics and Decay on Video Memorability
+](https://arxiv.org/pdf/2009.02568.pdf). European Conference on Computer Vision (ECCV), 2020.
+
+
+#### Task organizers
+* Alba García Seco de Herrera, University of Essex, UK, alba.garcia (at) essex.ac.uk
+* Rukiye Savran Kiziltepe, University of Essex, UK, rs16419 (at) essex.ac.uk
+* Sebastian Halder, University of Essex, UK
+* Ana Matrán Fernandez, University of Essex, UK
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+* Alan Smeaton, Dublin City University, Ireland
+* Claire-Hélène Demarty, InterDigital, R&I, France
+* Camilo Fosco, Massachusetts Institute of Technology Cambridge, Massachusetts, USA
+* Lorin Sweeney, Dublin City University, Ireland
+* Graham Healy, Dublin City University, Ireland
+
+#### Task Schedule (Updated)
+* 17 September: Data release
+* 18 November: Runs due
+* 22 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December 2021: MediaEval 2021 Workshop Online
diff --git a/_editions/2021/tasks/music.md b/_editions/2021/tasks/music.md
new file mode 100755
index 000000000..7030f5661
--- /dev/null
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@@ -0,0 +1,85 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: Emotions and Themes in Music
+subtitle: Emotion and Theme Recognition in Music using Jamendo
+blurb: We invite the participants to try their skills in building a classifier to predict the emotions and themes conveyed in a music recording, using our dataset of music audio, pre-computed audio features, and tag annotations (e.g., happy, sad, melancholic). All data we provide comes from Jamendo, an online platform for music under Creative Commons licenses.
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+
+Emotion and theme recognition is a popular task in music information retrieval that is relevant for music search and recommendation systems. We invite the participants to try their skills at recognizing moods and themes conveyed by the audio tracks.
+
+This task involves the prediction of moods and themes conveyed by a music track, given the raw audio. The examples of moods and themes are: happy, dark, epic, melodic, love, film, space etc. Each track is tagged with at least one tag that serves as a ground-truth.
+
+Participants are expected to train a model that takes raw audio as an input and outputs the predicted tags. To solve the task, participants can use any audio input representation they desire, be it traditional handcrafted audio features or spectrograms or raw audio inputs for deep learning approaches. We also provide a handcrafted feature set extracted by the [Essentia](https://essentia.upf.edu/documentation/) audio analysis library as a reference. We allow usage of third-party datsets for model development and training, but it needs to be mentioned explicitly.
+
+
+
+#### Target Group
+
+Researchers in music information retrieval, music psychology, machine learning, and music and technology enthusiasts in general.
+
+#### Data
+
+The dataset used for this task is the `autotagging-moodtheme` subset of the [MTG-Jamendo dataset](https://github.com/MTG/jamendo-dataset) [1], built using audio data from [Jamendo](https://jamendo.com) and made available under Creative Commons licenses. This subset includes 18,486 audio tracks with mood and theme annotations. In total, there are 57 tags, and tracks can possibly have more than one tag.
+
+We also provide pre-computed statistical features from [Essentia](https://essentia.upf.edu) using the feature extractor for [AcousticBrainz](https://acousticbrainz.org/). These features are were previously used in the MediaEval genre recognition tasks in [2017](https://multimediaeval.github.io/2017-AcousticBrainz-Genre-Task/) and [2018](https://multimediaeval.github.io/2018-AcousticBrainz-Genre-Task/).
+
+
+#### Evaluation Methodology
+
+Participants should generate predictions for the [test split](https://github.com/MTG/jamendo-dataset/blob/master/data/splits/split-0/autotagging_moodtheme-test.tsv) and submit those to the task organizers.
+
+The generated outputs for the test dataset will be evaluated according to the following metrics that are commonly used in the evaluation of auto-tagging systems: Macro **ROC-AUC** and **PR-AUC** on tag prediction scores. Leaderboard will be based on PR-AUC.
+
+For reference, here are the [2019](https://multimediaeval.github.io/2019-Emotion-and-Theme-Recognition-in-Music-Task/) and [2020](https://multimediaeval.github.io/2020-Emotion-and-Theme-Recognition-in-Music-Task/) editions of the task.
+
+
+#### References and recommended reading
+
+
+
+[1] Dmitry Bogdanov, Minz Won, Philip Tovstogan, Alastair Porter and Xavier Serra. 2019. [The MTG-Jamendo dataset for automatic music tagging](http://mtg.upf.edu/node/3957). Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019).
+
+[2] Dmitry Bogdanov, Alastair Porter, Philip Tovstogan and Minz Won. 2019. [MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo](http://ceur-ws.org/Vol-2670/MediaEval_19_paper_31.pdf). MediaEval 2019 Workshop.
+
+[3] Dmitry Bogdanov, Alastair Porter, Philip Tovstogan and Minz Won. 2020. [MediaEval 2020: Emotion and Theme Recognition in Music Using Jamendo](https://eigen.no/MediaEval_20_paper_7.pdf). MediaEval 2020 Workshop.
+
+[4] Mohammad Soleymani, Micheal N. Caro, Erik M. Schmidt, Cheng-Ya Sha and Yi-Hsuan Yang. 2013. [1000 songs for emotional analysis of music](https://ibug.doc.ic.ac.uk/media/uploads/documents/cmm13-soleymani.pdf). In Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia (CrowdMM 2013), 1-6.
+
+[5] Anna Aljanaki, Yi-Hsuan Yang and Mohammad Soleymani. 2014. [Emotion in music task at MediaEval 2014](http://ceur-ws.org/Vol-1263/mediaeval2014_submission_33.pdf).
+
+[6] Renato Panda, Ricardo Malheiro and Rui Pedro Paiva. 2018. [Musical texture and expressivity features for music emotion recognition](http://mir.dei.uc.pt/pdf/Conferences/MOODetector/ISMIR_2018_Panda.pdf). In Proceedings of the International Society on Music Information Retrieval Conference (ISMIR 2018), 383-391.
+
+[7] Cyril Laurier, Owen Meyers, Joan Serra, Martin Blech and Perfecto Herrera. 2009. [Music mood annotator design and integration](http://mtg.upf.edu/files/publications/Laurier_MusicMoodAnnotator.pdf). In 7th International Workshop on Content-Based Multimedia Indexing (CBMI'09), 156-161.
+
+[8] Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Brandon G. Morton, Patrick Richardson, Jeffrey Scott, Jacquelin A. Speck and Douglas Turnbull. 2010. [Music emotion recognition: A state of the art review](http://ismir2010.ismir.net/proceedings/ismir2010-45.pdf). In Proceedings of the International Society on Music Information Retrieval Conference (ISMIR2010), 255-266.
+
+[9] Xiao Hu and J. Stephen Downie. 2007. [Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata](http://ismir2007.ismir.net/proceedings/ISMIR2007_p067_hu.pdf). In Proceedings of the International Conference on Music Information Retrieval (ISMIR2007), 67-72.
+
+
+#### Task Organizers
+Philip Tovstogan, Music Technology Group, Universitat Pompeu Fabra, Spain
+Dmitry Bogdanov, Music Technology Group, Universitat Pompeu Fabra, Spain
+Alastair Porter, Music Technology Group, Universitat Pompeu Fabra, Spain
+(first.last@upf.edu)
+
+
+
+
+#### Task Schedule (Updated)
+* 1 June: Data releases
+* 12 November: Runs due
+* 19 November: Results returned
+* 29 November 2021: Working notes paper due
+* 13-15 December 2021: MediaEval 2021 Workshop Online
+
+Workshop will be held online. Exact dates to be announced.
diff --git a/_editions/2021/tasks/newsimages.md b/_editions/2021/tasks/newsimages.md
new file mode 100644
index 000000000..52e8dd06c
--- /dev/null
+++ b/_editions/2021/tasks/newsimages.md
@@ -0,0 +1,102 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "NewsImages"
+subtitle:
+blurb: "Images play an important role in online news articles and news consumption patterns. This task aims to achieve additional insight about this role. Participants are supplied with a large set of articles (including text body, and headlines) and the accompanying images. The task requires participants to predict which image was used to accompany each article and also predict frequently clicked articles on the basis of accompanying images."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+News articles use both text and images to communicate their message. The overall goal of this task is to better understand the relationship between the textual and visual (images) content of news articles, and the impact of these elements on readers’ interest in the news.
+
+Within this task participants are expected to discover and develop patterns/models to describe the relation between:
+* The images and the text of news articles (including text body, and headlines), and
+* The news items and the users’ interest in them (measured by the number of views).
+
+
+To do this, the participants will be provided a sizable real-world dataset of news items, each consisting of textual features (headline and snippet) as well the link to download the accompanying image.
+
+The task requires extracting features from visual images and textual descriptions. Participants must analyze the features' correlation concerning the context, noise, and the topic domain.
+
+The NewsImages task includes two subtasks: Image-Text Re-Matching and News Click Prediction. The participants can choose to participate in either or both subtasks.
+
+*Participants are encouraged to make their code public with their submission.*
+
+
+***Subtask 1: Image-Text Re-Matching:*** News articles often contain images that accompany the text. The connection between the images and the text is more complex than often realized. Aspects such as readers’ attention, difference between authentic imagery and stock photos, and placement on the website play important roles. We encourage participants to consider the explainability of their models. In this subtask, by using the news articles and accompanying images in the provided dataset, participants should predict which image was published with a given news article. We also ask participants to report their insights into characteristics that connect the text of news articles and the images. We expect that these insights contribute to the understanding of the image-text relationship in news articles.
+
+***Subtask 2: News Click Prediction:*** News websites present recommendations to users suggesting what to read next. These are often displayed as the article title accompanied by an image. In this task, participants investigate whether recommendations that are frequently clicked by users can be predicted using the textual content of the article and/or the accompanying image. Publishers tend to focus on click-related scores to determine the value of recommendations.
+
+#### Motivation and background
+Online news articles are multimodal: the textual content of an article is often accompanied by an image. The image is important for illustrating the content of the text, but also attracting readers’ attention. Research in multimedia and recommender systems generally assumes a simple relationship between images and text occurring together. For example, in image captioning [6], the caption is often assumed to describe the literally depicted content of the image. In contrast, when images accompany news articles, the relationship becomes less clear [8]. The goal of this task is to investigate these intricacies in more depth, in order to understand the implications that it may have for the areas of journalism and recommender systems.
+
+The task is formulated into two straightforward subtasks that participants can address using text-based and/or image features. However, the ultimate objective of this task is to gain additional insight. Specifically, we are curious about the connection between the textual content of articles and the images that accompany them and also about the connection between the image and title shown by a recommender system to users and the tendency of users to click on the recommended article. We are especially interested in aspects of images that go beyond the conventional set of concepts studied by concept detection. We are also interested in aspects of images that go beyond the literally depicted content. Such aspects include color, style, and framing.
+
+#### Target group
+This task targets researchers who are interested in the connection between images and text and images and user behavior. This includes people working in the areas of computer vision, recommender systems, cross-modal information retrieval, as well as in the area of news analysis.
+
+#### Data
+The data set is a large collection of news articles from a German publisher that publishes news article recommendations on its website. Each article consists of a headline and a text snippet (first 256 characters) plus the link to download the accompanying image. The data is split into a training set (ground truth provided) and a test set. Participants must crawl the images themselves as we lack the necessary copyright to provide them directly. To strictly ensure fair comparison, the final test set will include the test set articles for which all participants could successfully access the images.
+
+* Training: 15,000 (2 ¼ h to download)
+* Test: 5,000 (45min to download)
+* After the crawl stage, participants all send the list of images that they cannot access, and everyone throws these images out of their dataset, so that the official dataset for the year contains only images that all participants can access. A deadline for this process will be announced later on.
+
+
+
+#### Evaluation methodology
+***Subtask 1: Image-Text Re-Matching:*** For each news article in the test set, participants return the top five images that they predict to have accompanied that article. The ground truth (the correct news article-image-connection) is defined by the image that was published in the news article on the web portal.
+We encourage participants to additionally provide confidence scores such that we can learn more about the robustness of their methods. Success is measured with Precision@5. This means, that for each news item, 5 images should be suggested. If the correct images is in the suggested set, the predction is seen as correct. Since only one image per news item is correct, the metric could also be seen as Recall@5.
+Additionally, we promote the idea of explainability and ask the participants to look into the inner workings of their methods. What does the model tell? For which instances has the method failed and why?
+
+
+
+***Subtask 2: News Click Prediction:*** Given a set of images, participants predict the topmost news articles that are likely to be clicked when they are recommended. The number of top images will be specified. Success is measured by precision. More concretely, participants score each image which induces a ranking. We will determine the precision at a suited cut off point. Again, we encourage participants to examine their models and try to explain what they have picked up.
+
+*Analysis and Insight:* For both tasks, the ultimate goal is to understand news and news consumption behavior. We will also judge participants in terms of the quality of the insight that they achieve about the relationship between text and images and in the relationship between images and news consumption behavior.
+
+
+#### References and recommended reading
+
+
+[1] Corsini, Francesco, and Martha A. Larson. [CLEF NewsREEL 2016: image based recommendation.](https://repository.ubn.ru.nl/bitstream/handle/2066/161886/161886.pdf) (2016).
+
+[2] Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007, May). [Google news personalization: scalable online collaborative filtering](https://dl.acm.org/doi/abs/10.1145/1242572.1242610). In Proceedings of the 16th international conference on World Wide Web (pp. 271-280).
+
+[3] Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., & Huber, A. (2014, October). [Offline and online evaluation of news recommender systems at swissinfo.ch](https://dl.acm.org/doi/abs/10.1145/2645710.2645745). In Proceedings of the 8th ACM Conference on Recommender systems (pp. 169-176).
+
+[4] Ge, M., & Persia, F. (2017). [A survey of multimedia recommender systems: Challenges and opportunities.](https://www.worldscientific.com/doi/abs/10.1142/S1793351X17500039) International Journal of Semantic Computing, 11(03), 411-428.
+
+[5] Hopfgartner, F., Balog, K., Lommatzsch, A., Kelly, L., Kille, B., Schuth, A., & Larson, M. (2019). [Continuous evaluation of large-scale information access systems: a case for living labs.](https://link.springer.com/chapter/10.1007/978-3-030-22948-1_21) In Information Retrieval Evaluation in a Changing World (pp. 511-543). Springer, Cham.
+
+[6] Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019). [A comprehensive survey of deep learning for image captioning.](https://dl.acm.org/doi/abs/10.1145/3295748) ACM Computing Surveys (CSUR), 51(6), 1-36.
+
+[7] Lommatzsch, A., Kille, B., Hopfgartner, F., Larson, M., Brodt, T., Seiler, J., & Özgöbek, Ö. (2017, September). [CLEF 2017 NewsREEL overview: A stream-based recommender task for evaluation and education.](https://link.springer.com/book/10.1007/978-3-319-65813-1) In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 239-254). Springer, Cham.
+
+[8] Oostdijk, N., van Halteren, H., Bașar, E., & Larson, M. (2020, May). [The Connection between the Text and Images of News Articles: New Insights for Multimedia Analysis.](https://www.aclweb.org/anthology/2020.lrec-1.535/) In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 4343-4351).
+
+#### Task organizers
+* Andreas Lommatzsch, TU Berlin, Germany
+* Benjamin Kille, TU Berlin, Germany
+* Özlem Özgöbek, NTNU Trondheim, Norway
+* Duc Tien Dang Nguyen, University of Bergen, Norway
+* Mehdi Elahi, University of Bergen, Norway
+
+
+#### Task Schedule (Updated)
+* 30 June 2021: Data is made available
+* 07 November 2021: Runs due
+* 15 November 2021: Results returned
+* 29 November 2021: Working notes paper due
+* 13-15 December 2021: MediaEval 2021 Workshop Online
+
+
+
diff --git a/_editions/2021/tasks/sportsvideo.md b/_editions/2021/tasks/sportsvideo.md
new file mode 100644
index 000000000..88b31816b
--- /dev/null
+++ b/_editions/2021/tasks/sportsvideo.md
@@ -0,0 +1,104 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Sports Video: Fine Grained Action Detection and Classification of Table Tennis Strokes from videos"
+subtitle:
+blurb: "Participants are provided with a set of videos of table tennis games and are required to analyze them (i.e., carry out classification and detection of strokes). The ultimate goal of this research is to produce automatic annotation tools for sports faculties, local clubs and associations to help coaches to better assess and advise athletes during training."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+This task offers researchers an opportunity to test their fine-grained classification methods for detecting and recognizing strokes in table tennis videos. (The low inter-class variability makes the task more difficult than with usual general datasets like UCF-101.) The task offers two subtasks:
+
+***Subtask 1: Stroke Detection:*** Participants are required to build a system that detects whether a stroke has been performed, whatever its class, and to extract its temporal boundaries. The aim is to be able to distinguish between moments of interest in a game (players performing strokes) from irrelevant moments (between strokes, picking up the ball, having a break…). This subtask can be a preliminary step for later recognizing a stroke that has been performed.
+
+***Subtask 2: Stroke Classification:*** Participants are required to build a classification system that automatically labels video segments according to a performed stroke. There are 20 possible stroke classes.
+
+Compared with [Sports Video 2020](https://multimediaeval.github.io/editions/2020/tasks/sportsvideo/), this year we extend the task in the direction of detection and also enrich the dataset with new and more diverse stroke samples. The overview paper of the task is already available [here](https://www.labri.fr/projet/AIV/MediaEval/Sports_Video_Task_2021.pdf).
+
+Participants are encouraged to make their code public with their submission. We provide a public baseline, have a look [here](https://github.com/ccp-eva/SportTaskME21).
+
+#### Motivation and background
+Action detection and classification are one of the main challenges in visual content analysis and mining. Sports video analysis has been a very popular research topic, due to the variety of application areas, ranging from analysis of athletes’ performances and rehabilitation to multimedia intelligent devices with user-tailored digests. Datasets focused on sports activities or datasets including a large amount of sports activity classes are now available and many research contributions benchmark on those datasets. A large amount of work is also devoted to fine-grained classification through the analysis of sports gestures using motion capture systems. However, body-worn sensors and markers could disturb the natural behavior of sports players. Furthermore, motion capture devices are not always available for potential users, be it a University Faculty or a local sports team. Giving end-users the possibility to monitor their physical activities in ecological conditions through simple equipment is a challenging issue. The ultimate goal of this research is to produce automatic annotation tools for sports faculties, local clubs and associations to help coaches better assess and advise athletes during training.
+
+#### Target group
+The task is of interest to researchers in the areas of machine learning, visual content analysis, computer vision and sports performance. We explicitly encourage researchers focusing specifically in domains of computer-aided analysis of sports performance.
+
+#### Data
+Our focus is on recordings that have been made by widespread and cheap video cameras, e.g., GoPro. We use a dataset specifically recorded at a sports faculty facility and continuously completed by students and teachers. This dataset is constituted of player-centered videos recorded in natural conditions without markers or sensors. It comprises 20 table tennis strokes, and a rejection class. The problem is hence a typical research topic in the field of video indexing: for a given recording, we need to label the video by recognizing each stroke appearing in it. The dataset is subject to a specific usage agreement accesible [here](https://www.labri.fr/projet/AIV/MediaEval/Particular_conditions.pdf).
+
+#### Evaluation methodology
+Twenty stroke classes are considered according to the rules of table tennis. This taxonomy was designed with professional table tennis teachers. We are working on videos recorded at the Faculty of Sports of the University of Bordeaux. Students are the sportsmen filmed and the teachers are supervising exercises conducted during the recording sessions. The dataset has been recorded in a sports faculty facility using a light-weight equipment, such as GoPro cameras. The recordings are markerless and allow the players to perform in natural conditions from different viewpoints. These sequences were manually annotated, and the annotation sessions were supervised by professional players and teachers using a crowdsourced annotation platform.
+
+The training dataset shared for each subtask is composed of videos of table tennis matches with temporal borders of performed strokes supplied in an xml file, with the corresponding stroke label.
+
+***Subtask 1: Stroke Detection:*** Participants are asked to temporally segment regions where a stroke is performed on unknown videos of matches. The mAP and IoU metrics on temporal segments will be used for evaluation.
+
+***Subtask 2: Stroke Classification:*** Participants produce an xml file where each stroke of test sequences is labeled according to the given taxonomy. Submissions will be evaluated in terms of accuracy per class and global accuracy.
+
+For each subtask, participants may submit up to five runs. We also encourage participants to carry out a failure analysis of their results in order to gain insight into the mistakes that their classifiers make.
+
+#### Leaderboard
+
+##### Subtask 1: Stroke Detection
+
+| Rank | Team | mAP | Global IoU |
+| :--: | :--: | :-: | :--------: |
+| 1 | Baseline | 0.0173 | 0.144 |
+| 2 | QuantEx | 0.00124 | 0.070 |
+| 3 | SSNCSE | 0.000525 | 0.247 |
+
+##### Subtask 2: Stroke Classification
+
+| Rank | Team | Global Acc in % |
+| :--: | :--: | :--------: |
+| 1 | INF | 74.2 |
+| 2 | SELAB-HCMUS | 68.8 |
+| 3 | Baseline | 20.4 |
+| 4 | SSNCSE | 9.95 |
+
+#### References and recommended reading
+
+
+
+
+[1] [Crisp Project](https://github.com/P-eMartin/crisp)
+
+[2] Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. 2020. [Fine grained sport action recognition with siamese spatio-temporal convolutional neural networks](https://link.springer.com/epdf/10.1007/s11042-020-08917-3). Multimedia Tools and Applications 79, 2020, 20429–20447.
+
+[3] Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. [3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks](https://hal.archives-ouvertes.fr/hal-02977646/document). 2020 25th International Conference on Pattern Recognition (ICPR), 2021, 6019-6026.
+
+[3] Gül Varol, Ivan Laptev, and Cordelia Schmid. 2018. [Long-Term Temporal Convolutions for Action Recognition](https://arxiv.org/pdf/1604.04494.pdf). IEEE Trans. Pattern Anal. Mach. Intell. 40, 6 (2018), 1510–1517.
+
+[4] Joao Carreira and Andrew Zisserman. 2017. [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://arxiv.org/pdf/1705.07750.pdf). 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 4724-4733.
+
+[5] Chunhui Gu, Chen Sun, Sudheendra Vijayanarasimhan, Caroline Pantofaru, David A. Ross, George Toderici, Yeqing Li, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, and Jitendra Malik. 2017. [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](http://openaccess.thecvf.com/content_cvpr_2018/papers/Gu_AVA_A_Video_CVPR_2018_paper.pdf). 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, 6047-6056.
+
+[6] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. [UCF101: A dataset of 101 human actions classes from videos in the wild](https://arxiv.org/pdf/1212.0402.pdf). Computer Vision and Pattern Recognition (cs.CV), CRCV-TR-12-01.
+
+#### Task organizers
+You can email us directly at mediaeval.sport.task (at) diff.u-bordeaux.fr
+
+* Jordan Calandre, MIA, University of La Rochelle, France
+* Pierre-Etienne Martin, Max Planck Institute for Evolutionary Anthropology, Germany
+* Jenny Benois-Pineau, Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, France
+* Renaud Péteri, MIA, University of La Rochelle, France
+* Boris Mansencal, CNRS, Bordeaux INP, LaBRI, France
+* Julien Morlier, IMS, University of Bordeaux, France
+* Laurent Mascarilla, MIA, University of La Rochelle, France
+
+#### Task Schedule (Updated)
+* 1 August - 15 October 2021: Data release
+* 7 November 2021 ~~25 October 2021~~: Runs due
+* 8 November 2021: Results returned
+* 29 November 2021: Working notes paper
+* 13-15 December 2021: MediaEval 2020 Workshop
+
+#### Acknolwedgments
+We would like to thank all the players and annotators for their involvement in the acquisition and annotation processes and Alain Coupet from sports faculty of Bordeaux, expert and teacher in table tennis, for the proposed table tennis strokes taxonomy.
diff --git a/_editions/2021/tasks/template.md b/_editions/2021/tasks/template.md
new file mode 100644
index 000000000..32aba2bf3
--- /dev/null
+++ b/_editions/2021/tasks/template.md
@@ -0,0 +1,53 @@
+---
+# static info
+layout: task
+year: 2021
+hide: true
+
+# required info
+title:
+subtitle:
+blurb:
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+
+#### Motivation and background
+
+#### Introduction
+
+#### Target group
+
+#### Data
+
+#### Ground truth
+
+#### Evaluation methodology
+
+#### References and recommended reading
+
+
+
+#### Task organizers
+*
+*
+
+
+#### Task auxiliaries
+
+*
+*
+
+
+#### Task Schedule
+* XX XXX: Data release
+* XX November: Runs due
+* XX November: Results returned
+* 22 November: Working notes paper
+* Beginning December: MediaEval 2020 Workshop
+
+#### Acknolwedgments
+
diff --git a/_editions/2021/tasks/videoprivacy.md b/_editions/2021/tasks/videoprivacy.md
new file mode 100644
index 000000000..a40cda9c8
--- /dev/null
+++ b/_editions/2021/tasks/videoprivacy.md
@@ -0,0 +1,107 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Driving Road Safety Forward: Video Data Privacy"
+subtitle:
+blurb: "This task aims to explore methods for obscuring driver identity in driver-facing video recordings while preserving human behavioral information."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate. In addition, [register here](https://bit.ly/VideoDataPrivacy) to access data from the GitHub repository and receive announcements about Jupyter Notebook tutorials, team formation, and seed funding opportunities. *
+
+#### Task Description
+The goal of this video data task is to explore methods for obscuring driver identity in driver-facing video recordings while preserving human behavioral information.
+
+#### Motivation and background
+The lifetime odds for dying in a car crash are 1 in 107 [1]. Each year, vehicle crashes cost hundreds of billions of dollars [2]. Research shows that driver behavior is a primary factor in ⅔ of crashes and a contributing factor in 90% of crashes [3].
+
+Video footage from driver-facing cameras presents a unique opportunity to study driver behavior. Indeed, in the United States, the Second Strategic Highway Research Program (SHRP2) worked with drivers across the country to collect more than 1 million hours of driver video [4, 5]. Moreover, the growth of both sensor technologies and computational capacity provides new avenues for exploration.
+
+However, video data analysis and interpretation related to identifiable human subjects bring forward a variety of multifaceted questions and concerns, spanning privacy, security, bias, and additional implications [6]. This task aims to advance the state-of-the-art in video de-identification, encouraging participants from all sectors to develop and demonstrate techniques with the provided data. Successful methods balancing driver privacy with fidelity of relevant information have the potential to not only broaden researcher access to existing data, but also inform the trajectory of transportation safety research, policy, and education initiatives [7].
+
+#### Target group
+Participants of all experience levels and backgrounds with interests including de-identification techniques, video analytics, transportation safety, security, privacy, human behavior, and risk assessment are invited to engage in and contribute to this task. From expert researchers in academia and industry to students, nonprofit organizations, and government, all are encouraged to explore the data and submit approaches and technical demonstrations of driver de-identification.
+
+#### Data
+The dataset consists of both high- and low-resolution driver video data prepared by Oak Ridge National Laboratory for this Driver Video Privacy Task. The data were captured using the same data acquisition system as the larger SHRP2 dataset mentioned above, which currently has limited access in a secure enclave. For the data in this Task, there are drivers in choreographed situations designed to emulate different naturalistic driving environments. Actions include talking, coughing, singing, dancing, waving, eating, and various others [8]. Through this unique partnership, annotated data from Oak Ridge National Laboratory will be available to registered participants, alongside experts from the data collection and processing team who will be available for mentoring and any questions.
+
+#### Evaluation methodology
+The evaluation process includes a preliminary automated evaluation as well as a human evaluation, to assess the de-identification of faces and measure the consistency in preserving driver actions and emotions. An initial automated process will be run using a deep learning-based gaze estimator. The difference in predicted gaze-vectors from the original un-filtered video and de-identified video will be used as an initial score. Human evaluators will use the evaluation methodology as described by Baragchizadeh et al. in Evaluation of Automated Identity Masking Method (AIM) in Naturalistic Driving Study (NDS) [9].
+
+The scores for each of these areas will be combined for an overall assessment, prioritizing the human assessment of de-identification. PLEASE NOTE that this Task is heavily reliant on human evaluation, and we encourage participants to include in their submission any ideas, methods, and results from their own evaluation approaches. The participants’ descriptions of methodology, assumptions, and results will be shared with reviewers and the project organizers for additional discussion and opportunities for seed funding for further research.
+
+Although we encourage all Task participants to think creatively and holistically about how the expectations of privacy, the risk from potential attackers, and various threat models may evolve, our starting assumptions are that:
+(1) The drivers are not known to the potential attacker. We assume there is no relationship between the attacker and the driver. Furthermore, it is assumed that the driver is not a public figure.
+(2) Any information from the driver’s surroundings is assumed to not influence the attacker’s ability to identify the driver.
+(3) Access to the data is limited to registered users who have signed a Data Use Agreement specifying they will not attempt to learn the identity of individuals in the videos.
+(4) Attackers have access to basic computational resources.
+(5) There is a low probability of attackers launching an effective crowdsourcing strategy to re-identify the drivers, in part due to the Data Use Agreement and context in which the data were collected.
+
+The organizers of this Task encourage open source code with a MIT license, and the open sharing of insights to support a multidisciplinary community of practice. We anticipate that with the engagement of the MediaEval community there will be multiple opportunities to highlight both quantitative and qualitative feedback from participants, supporting reproducibility, open science, and future collaborative research.
+
+#### References and recommended reading
+
+
+
+[1] Odds of dying. (2021, March 04). Retrieved March 28, 2021, from https://injuryfacts.nsc.org/all-injuries/preventable-death-overview/odds-of-dying/
+
+[2] Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2015, May). The economic and societal impact of motor vehicle crashes, 2010. (Revised)(Report No. DOT HS 812 013). Washington, DC: National Highway Traffic Safety Administration.
+
+[3] Dingus, T., Guo, F., Lee, S., Antin, J., Perez, M., Buchanan-King, M., & Hankey, J. (2016, March 08). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Retrieved April 18, 2021, from https://www.pnas.org/content/113/10/2636
+
+[4] About safety Data: Strategic Highway research Program 2 (SHRP 2). Retrieved from http://www.trb.org/StrategicHighwayResearchProgram2SHRP2/SHRP2DataSafetyAbout.aspx
+
+[5] A Brief Look at the History of SHRP2 http://shrp2.transportation.org/pages/History-of-SHRP2.aspx
+
+[6] Finch, K. (2016, April 25). A visual guide to practical data de-identification. Retrieved March 28, 2021, from https://fpf.org/blog/a-visual-guide-to-practical-data-de-identification/
+
+[7] Exploratory Advanced Research Program Video Analytics Research Projects https://www.fhwa.dot.gov/publications/research/ear/15025/15025.pdf
+
+[8] Ferrell, R., Aykac, D., Karnowski, T., & Srinivas, N. (2021, January). A Publicly Available, Annotated Data Set for Naturalistic Driving Study and Computer Vision Algorithm Development. Retrieved from https://info.ornl.gov/sites/publications/Files/Pub122418.pdf
+
+[9] Baragchizadeh, Asal, O'Toole, Alice, Karnowski, Thomas Paul, & Bolme, David S. Evaluation of Automated Identity Masking Method (AIM) in Naturalistic Driving Study (NDS). United States. https://doi.org/10.1109/FG.2017.54
+
+
+#### Task organizers
+Please get in touch. Experts from the data collection and processing team are available for mentoring and any questions. We will be updating the information below throughout the summer.
+
+* Meredith Lee, University of California, Berkeley, USA mmlee (at) berkeley.edu
+* Gerald Friedland, University of California, Berkeley, USA fractor (at) berkeley.edu
+* Alex Liu, University of California, Berkeley, USA alexshiyuliu (at) berkeley.edu
+* Andrew Boka, University of California, Berkeley, USA
+* Arjun Sarup, University of California, Berkeley, USA
+
+
+
+
+
+
+
+
+#### Task Schedule (Updated)
+* July 2021: Registration on Submittable opens
+* July 2021: Data release to registered participants
+* August-October 2021: Community webinars/mentoring
+* 15 November 2021: Runs due
+* 23 November 2021: Results returned
+* 1 December 2021: Working notes paper
+* 13-15 December 2021: MediaEval 2021 Workshop
+
+#### Acknowledgments
+Special thanks to our collaborators, advisors, and mentors, including:
+
+
+Asal Baragchizadeh, School of Behavior and Brain Science, The University of Texas at Dallas\
+Alice O’Toole, School of Behavior and Brain Science, The University of Texas at Dallas\
+Thomas P. Karnowski, Oak Ridge National Laboratory\
+Regina Ferrell, Oak Ridge National Laboratory\
+Charles Fay, U.S. Department of Transportation, Federal Highway Administration\
+David Kuehn, U.S. Department of Transportation, Federal Highway Administration\
+as well as Natalie Evans Harris, Lauren Smith, René Bastón, David E. Culler, and the NSF Big Data Hubs network
+
+This effort is made possible through community volunteers and National Science Foundation Grants 1916573, 1916481, and 1915774.
+
diff --git a/_editions/2021/tasks/visualsentiment .md b/_editions/2021/tasks/visualsentiment .md
new file mode 100644
index 000000000..1d6e4f31d
--- /dev/null
+++ b/_editions/2021/tasks/visualsentiment .md
@@ -0,0 +1,68 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Visual Sentiment Analysis: A Natural Disaster Use-case"
+subtitle:
+blurb: "The Visual Sentiment Analysis task aims at finding methods that can predict the emotional response from disaster-related images."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+Disaster-related images are complex and often evoke an emotional response, both good and bad. This task focuses on performing visual sentiment analysis on images collected from disasters across the world.
+
+The images contained in the provided dataset aim to provoke an emotional response through both intentional framining and based on the contents itself.
+
+*Subtask 1: Single-label Image Classification* The first task aims at a single-label image classification task, where the images are arranged in three different classes, namely positive, negative, and neutral with a bias towards the negative samples, due to the topic taken into consideration.
+
+*Subtask 2: Multi-label Image Classification* This is a multi-label image classification task where the participants will be provided with multi-labeled images. The multi-label classification strategy, which assigns multiple labels to an image, better suits our visual sentiment classification problem and is intended to show the correlation of different sentiments. In this task seven classes, namely joy, sadness, fear, disgust, anger, surprise, and neutral, are covered.
+
+*Subtask 3: Multi-label Image Classification* The task is also a multi-label, however, a wider range of sentiment classes are covered. Going deeper in the sentiment hierarchy, the complexity of the task increases. The sentiment categories covered in this task include anger, anxiety, craving, empathetic pain, fear, horror, joy, relief, sadness, and surprise.
+
+*Participants are encouraged to make their code public with their submission.*
+
+#### Motivation and background
+As implied by the popular proverb "a picture is worth a thousand words," visual contents are an effective means to convey not only facts but also cues about sentiments and emotions. Such cues representing the emotions and sentiments of the photographers may trigger similar feelings from the observer and could be of help in understanding visual contents beyond semantic concepts in different application domains, such as education, entertainment, advertisement, and journalism. To this aim, masters of photography have always utilized smart choices, especially in terms of scenes, perspective, angle of shooting, and color filtering, to let the underlying information smoothly flow to the general public. Similarly, every user aiming to increase in popularity on the Internet will utilize the same tricks. However, it is not fully clear how such emotional cues can be evoked by visual contents and more importantly how the sentiments derived from a scene by an automatic algorithm can be expressed. This opens an interesting line of research to interpret emotions and sentiments perceived by users viewing visual contents.
+
+#### Target group
+The task is appropriate for researchers in machine learning, multimedia retrieval, sentiment analysis, and visual analysis.
+
+#### Data
+We provide a slightly modified version of our visual sentiment analysis dataset [1], including a different training and testing set, consisting of disaster-related images collected from social media platforms such as Google, Flickr, and Twitter.
+
+#### Ground truth
+The dataset was annotated through a crowd-sourcing study using Microworkers, where at least five different participants were assigned to annotate each image. The final tags were chosen based on a majority vote from the five participants assigned to it. The study concluded with 10,010 different responses from 2,338 participants. The participants included individuals from different age groups and 98 countries. The time spent by a participant on an image, which helped filter out careless or inappropriate responses. Before the study, two trial studies were performed to test, correct errors, and improve clarity and readability.
+
+
+#### Evaluation methodology
+All the tasks will be evaluated using standard classification metrics, where weighted F1-Score will be used to rank the different submissions. We also encourage participants to carry out a failure analysis of the results to gain insight into why a classifier may make a mistake.
+
+#### References and recommended reading
+
+
+[1] [Hassan, Syed Zohaib, et al. "Visual Sentiment Analysis from Disaster Images in Social Media." arXiv preprint arXiv:2009.03051 (2020).](https://arxiv.org/pdf/2009.03051.pdf)
+
+[2] [Hassan, Syed Zohaib, et al. "Sentiment analysis from images of natural disasters." International Conference on Image Analysis and Processing. Springer, Cham, 2019.](https://arxiv.org/abs/1910.04416)
+
+[3] [Ortis, Alessandro, Giovanni Maria Farinella, and Sebastiano Battiato. "Survey on visual sentiment analysis." IET Image Processing 14.8 (2020): 1440-1456.](https://arxiv.org/pdf/2004.11639.pdf)
+
+#### Task organizers
+* Kashif Ahmad, kahmad (at) hbku.edu.qa, Hamad Bin Khalifa University, Doha, Qatar
+* Michael Riegler, michael (at) simula.no, SimulaMet, Norway
+* Zohaib Hassan, syed (at) simula.no, SimulaMet and OsloMet, Norway
+* Steven Hicks, steven (at) simula.no, SimulaMet and OsloMet, Norway
+* Nicola Conci, nicola.conci (at) unitn.it, University of Trento, Italy
+* Pål Halvorsen, paalh (at) simula.no, SimulaMet and OsloMet, Norway
+* Ala Al-Fuqaha, aalfuqaha (at) hbku.edu.qa, Hamad Bin Khalifa University, Doha, Qatar
+
+#### Task Schedule (Updated)
+* August : Data release
+* 15 November: Runs due
+* 20 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December: MediaEval 2021 Workshop
diff --git a/_editions/2021/tasks/watermm.md b/_editions/2021/tasks/watermm.md
new file mode 100644
index 000000000..a10822f69
--- /dev/null
+++ b/_editions/2021/tasks/watermm.md
@@ -0,0 +1,73 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "WaterMM: Water Quality in Social Multimedia"
+subtitle: "Relevance classification of bilingual social multimedia for water quality"
+blurb: "The quality of drinking water can have a direct effect on the health of people. In this task, the participants are asked to automatically determine which social media posts (i.e., tweets) are relevant to water quality, safety and security, by using their text, images and metadata. The dataset is bilingual (i.e., English and Italian tweets), while the ground truth labels have been provided by experts in the water domain."
+---
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+The WaterMM Task deals with the analysis of social media posts from Twitter with regards to issues of water quality, safety and security. The participants of this task are provided with a set of Twitter post IDs in order to download the text, the attached image and the metadata of tweets that have been selected with keyword-based search that involved words/phrases about the quality of drinking water (e.g., strange color, smell or taste, related illnesses, etc.). Because the occurrence of such phrases in a tweet might not necessarily reflect a case of water contamination, participants are asked to build a binary classification system that will be able to distinguish whether a post is relevant or not to water-quality issues.
+
+The dataset is bilingual (English and Italian), and participants can tackle the task by using textual information, visual information, metadata or a combination of the above. In addition, participants are highly encouraged to make their code public along with their submission.
+
+#### Motivation and background
+The rise of social media has led to discussion of a broad range of topics related to everyday life. One of the topics that we expect to be mentioned in social media posts is water quality, safety and security. The acquisition of posts containing citizen complaints on the condition of drinking water, as an addition to traditional means such as phone calls, could support situational awareness in a water distribution network. However, within the post stream we expect that a number of posts containing water-quality-related keywords does not refer to actual cases of polluted water. To minimize the incoming noise, automatic prediction of a post’s relevance is required. Filtering out irrelevant posts will improve the quality of the information that interested organizations, such as water utilities or water protection agencies, receive from social media.
+
+Research on developing classifiers for social media monitoring is often related to *sudden crisis* (see last year's [Flood-related Multimedia Task](https://multimediaeval.github.io/editions/2020/tasks/floodmultimedia/)). Studying water quality allows us to expand the scope in order to include the so-called *creeping crisis*, i.e., a dangerous situation that emerges slowly.
+
+Moreover, the dataset is bilingual (English and Italian) to encourage researchers to tackle the real-world challenge of multiple languages in the data.
+
+#### Target group
+Researchers in the areas of social media, multimedia and multilingual analysis, multimedia classification and information retrieval are strongly encouraged to participate in the WaterMM Task. Industries and SMEs that develop similar AI technologies for semantic data fusion and retrieval of multi- or cross-lingual content are also warmly invited to participate. Furthermore, the challenge could be interesting to researchers and practitioners in water-related domains, such as water engineering, water distribution, and water management in general.
+
+#### Data
+The dataset is a set of social media posts collected from Twitter during one year (from May 2020 to April 2021) by searching for English and Italian keywords inside the tweet text about water quality (e.g. issues with drinking water, signs of water pollution, illnesses related to water). In order to be fully compliant with the Twitter Developer Policy, only the IDs of the tweets are distributed to the participants, but a tool to download them is also provided.
+
+#### Ground truth
+The ground truth of the dataset reflects the relevance of a tweet (relevant / not relevant) and has been manually collected with human annotation, realized by the Eastern Alps River Basin District, who are responsible for hydrogeological defense, i.e., the protection of water resources and aquatic environments, in the Eastern Alps partition of North-East Italy.
+
+#### Evaluation methodology
+The evaluation metric for the binary classification of tweets as relevant (1) or not relevant (0) will be F-score. Participants are also encouraged to carry out a failure analysis of their results in order to gain insight in the mistakes that their classifiers make.
+
+#### References and recommended reading
+[1] Anastasia Moumtzidou, Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2018. [Flood relevance estimation from visual and textual content in social media streams](https://dl.acm.org/doi/abs/10.1145/3184558.3191620). In *Companion Proceedings of the The Web Conference 2018*, April 23, 2018, 1621-1627.
+
+[2] Abhishek Sharma, Yuan Tian, and David Lo. 2015. [NIRMAL: Automatic identification of software relevant tweets leveraging language model.](https://ieeexplore.ieee.org/document/7081855) In *2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)*, 449-458.
+
+[3] Alfredo Cobo, Denis Parra, D., and Jaime Navón. 2015. [Identifying relevant messages in a twitter-based citizen channel for natural disaster situations](https://dl.acm.org/doi/abs/10.1145/2740908.2741719). In *Proceedings of the 24th International Conference on World Wide Web*, May 18, 2015, 1189-1194.
+
+[4] Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, and Beatriz De La Iglesia. 2019. [Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210689). *PLOS One*, 14, Article 7.
+
+We also recommend to read past years’ task papers in the MediaEval Proceedings.
+
+#### Task organizers
+* Stelios Andreadis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece, [andreadisst@iti.gr](mailto:andreadisst@iti.gr)
+* Ilias Gialampoukidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Anastasia Moumtzidou, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Anastasios Karakostas, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Stefanos Vrochidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Ioannis Kompatsiaris, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Roberto Fiorin, Eastern Alps River Basin District, Italy
+* Francesca Lombardo, Eastern Alps River Basin District, Italy
+* Daniele Norbiato, Eastern Alps River Basin District, Italy
+* Michele Ferri, Eastern Alps River Basin District, Italy
+
+#### Task Schedule (Updated)
+* 23 July 2021: Development set release
+* 11 October 2021: Test set release
+* 10 November 2021: Runs due
+* 19 November 2021: Results returned
+* 29 November 2021: Working notes paper
+* 13-15 December 2021: MediaEval 2020 Workshop
+
+#### Acknowledgments
+This task has been supported by the EU’s Horizon 2020 research and innovation programme under grant agreements H2020-832876 [aqua3S](https://aqua3s.eu/), H2020-883484 [PathoCERT](https://pathocert.eu/), and H2020-101004157 [WQeMS](https://wqems.eu/project).
+
+
diff --git a/_editions/2021/tasks/wellbeing.md b/_editions/2021/tasks/wellbeing.md
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+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Insight for Wellbeing: Cross-Data Analytics for (transboundary) Haze Prediction"
+subtitle:
+blurb: "The task is organized as multiple subtasks to encourage multi-disciplinary research to consider additional data sources (cross-data) to improve prediction and/or find insights for wellbeing based on environmental factors, satellite remote sensing, social/news data, etc. The problems this task tries to tackle are \"air pollution\" and \"transboundary haze\"."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+Task participants will work on developing models for transboundary haze prediction using timeseries data describing air pollution concentration changes over time, weather data, and other related data sources, which are recorded at multiple locations across countries in the [ASEAN](https://en.wikipedia.org/wiki/ASEAN) region.
+
+
+Haze air pollution describes the pollution consisting of particulate matter of smoke, dust, and other vapours present in the air, which originate from large-scale forest and land fires, factories, and cars. When this mixture of air-borne pollutants reaches high levels it causes many health problems (respiratory infections, cardiovascular and lung diseases) especially among people who are already ill, and has negative impacts on visibility, economic production, transportation, and tourism. When haze pollution remains measurable at high levels after crossing into another country’s air space, it is referred to as a transboundary haze problem.
+
+
+The task is organized as multiple subtasks to encourage multi-disciplinary research that can consider additional data sources (cross-data) to improve prediction and/or find insights based on environmental factors, satellite remote sensing, social/news data, etc.
+
+Participants in this task will tackle three subtasks:
+
+*Subtask 1: 3-Day Localized Air Pollution Prediction:* in this subtask, the objective is to predict the PM10 value at different locations in multiple countries using data only from each country itself. This subtask will explore the accuracy of predicting air pollution for a few days ahead (currently 3), and it is designed to evaluate how well this objective can be achieved if each country depends only on its own direct weather data.
+
+*Subtask 2: 3-Day Transboundary Air Pollution Prediction:* the objective in this subtask is to predict the PM10 value in multiple countries by considering other data sources available from the same country or neighboring countries (remote sensing, social media streams, news, etc.). This subtask is encouraged to address transboundary haze effects, for example by observing the improvements of prediction accuracy once the haze and weather situation (e.g. wind/fire information) in neighboring countries is taken into account, or through other insights and conclusions that the participants find.
+
+*Subtask 3: Transfer Learning:* in this subtask, participants will re-visit subtasks 1 or 2 above, by considering transfer learning techniques in their solutions using pre-trained models for predicting new data, or by using models from subtask-1 in subtask-2, etc. The application of transfer learning can demonstrate that certain patterns learnt from other regions’ data sources or models (eg. via access to larger datasets) help in improving predictions for different regions (eg. where data is scarce or less granular).
+
+
+#### Motivation and background
+In the past decade, many countries especially in the ASEAN region have reported increased probabilities of cardiovascular and respiratory diseases, and haze pollution will affect patients with such diseases, and will have a negative impact on the well-being of citizens and tourists, social and economic activities. The accurate prediction of the haze situation for multiple time steps (hours or days) can help personal and public health advice and decision making (e.g. planning outdoor activities, closure of schools) in the event of predicted prolonged dangerous levels of haze. Tapping into multiple sources from ground instruments, remote sensing mechanisms, and other data streams, in addition to improved modeling techniques can help scientists track pollutants spread and provide better forecasting systems.
+
+Transboundary haze problem refers to the situation where haze originating at one country or region remains at high levels after crossing into other countries, resulting a recurrent issue in many regions in the world, especially in Southeast Asia where the sources contributing to haze pollution differ at each country with varying percentages coming from localized or transboundary sources. For example, transboundary (haze) pollution episodes are often attributed to the long-range transport of biomass fires from slash-and-burn activities during dry seasons or from forest fires, which travel depending on weather conditions to affect several neighboring countries.
+
+Particulate matter concentrations (PM10, PM2.5) and other gases concentrations are usually used to calculate air quality index measures that describe pollutant severity. Past recorded data of air pollution and meteorological parameters have been used by researchers and practitioners from academia and government agencies to develop air pollution prediction models to forecast changes in air pollution. Some studies used mathematical statistical models [1] to make long-term predictions of particulate matter by using climate models or satellite remote sensing. Besides, several machine learning methods [2] were used to improve accuracy in short-term prediction with the inclusion of meteorological data. Deep learning techniques, such as hybrid of convolutional neural network and long short-term memory methods were used, as proposed in [3,4] by Zhao and Zettsu on air pollution data from Japan, and by Yang et al. in 2020 [5] to predict hourly particulate matter concentrations in South Korea. Comparisons among multiple methods on daily air pollution prediction in Brunei were presented in 2020 [6] by Aziz et. al.
+
+#### Target group
+This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, data science, event-based processing and analysis, urban computing, environmental and atmospheric sciences.
+
+#### Data
+The data that is provided will include daily readings of PM10 concentrations recorded by multiple weather stations in ASEAN countries (Brunei, Singapore, and Thailand), covering different periods between 2010 and 2019 . The data also includes atmospheric parameters of daily (and in some cases hourly) values of temperature, rainfall, humidity, and wind speed/direction. However, not all parameters are available at every station/country, with some missing values in the data, which add to the challenge of this task.
+
+All data will be provided in CSV format, without inclusion of any Personal data. Task participants must sign the agreement for using the data prepared by MediaEval and NICT/UTB, which limit the use of the data for this competition only.
+
+#### Evaluation methodology
+Participants are encouraged to participate in all three subtasks described above. However, it is still accepted to submit solutions for only one or two of them (any subtask). There will be two types of prizes: most accurate models (marks will be assigned and averaged over all subtasks); and most innovative out-of-the-box idea (across subtasks as well).
+
+There will be two timeseries datasets, one for training and one for testing, for each country. Separate data files will include air pollution readings and weather parameters, which are time stamped and with location information.
+
+The testing datasets will be created from the ending tail of the timeseries data in each station/country, and will have some values hidden (multiple 3-day windows) which the participants will need to predict and use as their submissions for the two involved subtasks. The predicted values will be compared with the (kept aside) ground truth values, where the metrics of root mean squared error (RMSE), symmetric mean absolute percentage error (SMAPE), and mean absolute error (MAE) will be calculated and averaged for all stations in each country.
+
+Accurate predictions are supposed to have the smallest possible error values on each of the metrics across all stations in a single country. Participants may opt to use the same or different methods for different countries in their predictions. It is expected that the models should be accurate across countries as well, so the average of the errors over countries will be computed as a general indicator. However, individual accurate models per country are the focus.
+It is to be emphasized that for subtask-1 (localized air pollution), participants should use only the data from each specific country when developing their models and performing test data predictions. However, for subtask-2 and subtask-3, participants are expected to utilize all available data (across countries), and potentially other data sources that help in capturing the transboundary nature of the problem and provide better predictions.
+
+At the end of the challenge, participants are expected to submit their source code and any other necessary resources, together with their testing files predictions, in order to validate the applied methodologies.
+
+
+#### References and recommended reading
+
+
+[1] Aaron van Donkelaar, Randall V. Martin, Michael Brauer, Ralph Kahn, Robert Levy, Carolyn Verduzco, and Paul J. Villeneuve. 2010. [Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898863/), Environment Health Perspectives, 118(6), 847-855.
+
+[2] Sharjil Saeed, Lal Hussain, Imtiaz Ahmed Awan, and Adnan Idris. 2017. [Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours](http://paper.ijcsns.org/07_book/201711/20171106.pdf), International Journal of Computer Science and Network Security, 17(11), 45–52.
+
+[3] Peijiang Zhao and Koji Zettsu. 2018. [Convolution recurrent neural networks for short-term prediction of atmospheric sensing data](https://ieeexplore.ieee.org/document/8726777), 2018 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 30th July–3rd August 2018, Halifax, NS, Canada.
+
+[4] Peijiang Zhao and Koji Zettsu. 2019. [Convolution recurrent neural networks based dynamic transboundary air pollution prediction](https://ieeexplore.ieee.org/document/8712835), 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 15th March–18th March 2019, Suzhou, China.
+
+[5] Guang Yang, HwaMin Lee, and Giyeol Lee. 2020. [A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea](https://www.mdpi.com/2073-4433/11/4/348), Atmosphere 2020, 11(4), 348–367.
+
+[6] Effa Nabilla Aziz, Asem Kasem, Wida Susanty Haji Suhaili, and Peijiang Zhao. 2021. [Convolution Recurrent Neural Network for Daily Forecast of PM10 Concentrations in Brunei Darussalam](https://www.aidic.it/cet/21/83/060.pdf). Journal of Chemical Engineering Transactions, 2021, Vol. 83, pp. 355–360.
+
+[7] Minh-Son Dao, Peijiang Zhao, Tomohiro Sato, Koji Zettsu, Duc-Tien Dang-Nguyen, Cathal Gurrin, Ngoc-Thanh Nguyen. 2019. [Overview of MediaEval 2019: Insights for Wellbeing Task Multimodal Personal Health Lifelog Data Analysis](http://ceur-ws.org/Vol-2670/MediaEval_19_paper_10.pdf).
+
+[8] Peijiang Zhao, Minh-Son Dao, Thanh Nguyen, Thanh-Binh Nguyen, Duc-Tien Dang-Nguyen, and Cathal Gurrin. 2020. [Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis](https://www.eigen.no/MediaEval_20_paper_11.pdf).
+
+[9] Dat Q. Duong, Quang M. Le, Tan-Loc Nguyen-Tai, Dong Bo, Dat Nguyen, Minh-Son Dao, and Binh T. Nguyen. 2020. [Multi-source Machine Learning for AQI Estimation](https://ieeexplore.ieee.org/document/9378322), IEEE Big Data 2020.
+
+[10] Peijiang Zhao and Koji Zettsu. 2020. [MASTGN: Multi-Attention Spatio-Temporal Graph Networks for Air Pollution Prediction](https://ieeexplore.ieee.org/document/9378156), IEEE Big Data 2020.
+
+[11] Phuong-Binh Vo, Trong-Dat Phan, Minh-Son Dao, and Koji Zettsu. 2019. [Association Model between Visual Feature and AQI Rank Using Lifelog Data]( https://ieeexplore.ieee.org/abstract/document/9005636), IEEE Big Data 2019.
+
+[12] Dona Rofithoh Don Ramli and Rugayah Hashim. 2020. [National Interest Versus Regional Interest: The Case of Transboundary Haze Pollution](https://link.springer.com/chapter/10.1007/978-981-15-3859-9_12). Charting a Sustainable Future of ASEAN in Business and Social Sciences, pp 123-132.
+
+
+#### Task organizers
+* Asem Kasem, Universiti Teknologi Brunei (UTB), Brunei Darussalam, asem(dot)kasem(at)utb(dot)edu(dot)bn
+* Minh-Son Dao, National Institute of Information and Communications Technology, Japan (NICT), dao(at)nict(dot)go(dot)jp
+* Ngoc-Thanh Nguyen, Western Norway University of Applied Sciences, Norway (HVL), ntng(at)hvl(dot)no
+* Duc-Tien Dang-Nguyen, University of Bergen, Norway (UiB)
+* Cathal Gurrin, Dublin City University, Ireland (DCU)
+* Tran Minh Triet, University of Science, Vietnam (HCMUS)
+* Nguyen Thanh Binh, University of Science, Vietnam (HCMUS)
+* Wida Suhaili, Universiti Teknologi Brunei (UTB)
+
+#### Task Schedule (Updated)
+* 01 July: Data release
+* 14 November: Runs due + Start writing working notes paper
+* 19 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December: MediaEval 2021 Workshop
+
+
+
diff --git a/_editions/2022.md b/_editions/2022.md
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+---
+layout: edition
+title: MediaEval 2022
+year: 2022
+permalink: /editions/2022/
+---
+
+The MediaEval Multimedia Evaluation benchmark offers challenges in artificial intelligence for multimedia data. Participants address these challenges by creating algorithms for retrieval, analysis, and exploration. Solutions are systematically compared using a common evaluation procedure, making it possible to establish the state of the art and track progress. Our larger aim is to promote reproducible research that makes multimedia a positive force for society.
+
+MediaEval goes beyond other benchmarks and data science challenges in that it also pursues a “Quest for Insight” (Q4I). With Q4I we push beyond only striving to improve evaluation scores to also working to achieve deeper understanding about the challenges. For example, properties of the data, strengths and weaknesses of particular types of approaches, and observations about the evaluation procedure.
+
+The MediaEval 2022 Workshop will be held 12-13 January 2023, collocated with [MMM 2023](https://www.mmm2023.no) in Bergen, Norway and also online. For the preliminary workshop proceedings and the workshop schedule, please see the [MediaEval Workshop Information Announcement](https://multimediaeval.github.io/2023/01/06/workshop-information.html)
+
+
+
+
+
+
+
+
+### Workshop
+
+
+* Workshop schedule is here: [MediaEval 2022 Workshop Program](https://multimediaeval.github.io/editions/2022/docs/MultimediaEval_2022_Detailed_Program.pdf)
+* Proceedings: [MediaEval 2022 Working Notes Proceedings](https://ceur-ws.org/Vol-3583/)
+
+Workshop group photo:
+
+
+
+### Task schedule
+
+* June 2022: Sign up opens for participation in MediaEval 2022
+* June-August 2022: Data releases
+* Beginning November 2022: Runs due (see task pages for the deadline for each task)
+* 28 November 2022: Working notes paper due
+* 12-13 January 2023: MediaEval 2022 Workshop, Collocated with [MMM 2023](https://www.mmm2023.no) in Bergen, Norway and also online.
+
+##### The MediaEval Coordination Committee (2022)
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Steven Hicks, SimulaMet, Norway
+* Martha Larson, Radboud University, Netherlands (Overall coordinator and main contact person)
+
+MediaEval is grateful for the support of [ACM Special Interest Group on Multimedia](http://sigmm.org/)
+
+
+
+
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+
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+This folder contains `Markdown` (.md) files to all tasks for 2022 MediaEval edition.
+
+## How to edit
+
+Opening a file and clicking on the pencil logo (view Figure 1 below)
+
+
+
+you will access `edit` mode on the file aand you will see something like below:
+
+
+
+There are 2 main parts to the document:
+
+* part 1 (lines 1 to 11) is the task file metadata. Here, you (task organizer), should fill in all `# required info` fields (title, subtitle, and blurb). When your task content is ready to be published on the website, to be shown on the website, then you should edit the `hide` property to `false`, this way your task will be visible on the website.
+
+* part 2 (lines 12 to infinity) is the actual task content. There is a suggested structure to the document to be followed. This part accepts content with [Markdown](https://daringfireball.net/projects/markdown/syntax) and HTML syntax.
+
+After you fill all content `commit changes` by filling the form below that edit screen and clicking on `Propose changes` as shown in Figure 3 below:
+
+
+
+That action will open a new window in which you will confirm a `Pull request`. As you can see in Figure 4 below:
+* yellow arrow points out where you can select a reviewer (if you are already talking to one of the website admins), this is optional
+* fill your comments on the `fill here` space as you believe it's required to support approval of your change.
+* red arrow points to the button that confirms your `Pull request`
+
+
+
+Other than that please feel free to ask for help. This structure is and experiment and we need help to turn it useful and easy to everyone. MediaEval organizers are available to help or submit questions and issues [here](https://github.com/multimediaeval/multimediaeval.github.io/issues).
diff --git a/_editions/2022/tasks/disastermm.md b/_editions/2022/tasks/disastermm.md
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+---
+# static info
+layout: task
+year: 2022
+hide: false
+
+# required info
+title: "DisasterMM: Multimedia Analysis of Disaster-Related Social Media Data"
+subtitle:
+blurb: "Contribute to disaster management by addressing two subtasks: Classify multimodal twitter data as relevant or non-relevant to flooding events and and develop a named-entity recognizer in order to identify which words (or sequence of words) in a tweet’s text refer to locations."
+---
+
+
+*See the [MediaEval 2022 webpage](https://multimediaeval.github.io/editions/2022/) for information on how to register and participate.*
+
+#### Task Description
+The DisasterMM task involves multimedia analysis of social media data, specifically posts from the popular platform of Twitter, that relate to natural or manmade disasters. This year we focus on floods. The participants of this task are provided with a set of Tweet IDs from which they download a data set. The data set contains textual as well as visual information and other metadata. The tweets have been selected using keyword-based search that involved words/phrases about flood. DisasterMM includes two subtasks.
+
+* *Relevance Classification of Twitter Posts* (RCTP): participants build a binary classification system that will be able to distinguish whether a tweet is relevant or not to flooding incidents.
+* *Location Extraction from Twitter Texts* (LETT): participants develop a named-entity recognition model in order to identify which words (or sequence of words) inside a tweet’s text refer to locations.
+
+For both subtasks, the dataset is in Italian language, which supports the research community to move beyond a focus on English-language social media for social media analysis tasks.
+
+#### Motivation and background
+Flooding is considered the deadliest type of severe weather and it can have devastating effects on the society. Besides loss of lives and property damage, floods can also lead to secondary consequences, such as long-term displacement of residents and spread of waterborne diseases. In the last years, social media data and crowdsourcing in general have been explored by first responders and civil protection authorities as an alternative source of information, complementary to traditional means such as telephone, in order to raise the situational awareness and support their operations. In parallel, the scientific society has been proposing AI and machine learning solutions that improve the quality of the incoming social media data.
+
+Nevertheless, exploiting user-generated content from social media platforms comes with two significant limitations. First, the large and continuous streams of published posts can be very noisy, with messages that do not refer to actual cases of floods, but contain flood-related words in a different context (e.g. in a metaphorical way). Second, the majority of posts are not geotagged (i.e. not associated with a geographic position) or their geoinformation is questionable.
+
+The automatic prediction of a post’s relevance could reduce the social media noise and thus assist the interested parties in receiving only useful information, without spending time on filtering out unrelated messages. In addition, recognizing the locations that are mentioned inside the post’s text could enhance the post with geographic information, which would allow the automatic positioning of a potential incident. By receiving solely high-quality and geotagged social data, disaster management practitioners will be able to manage their resources more efficiently, which could even lead to saving more human lives.
+
+Furthermore, we would like to motivate researchers to move beyond English and investigate another language, in this case, Italian.
+
+#### Target group
+Researchers in the areas of social media, multimedia and multilingual analysis, multimedia classification, named-entity recognition and information retrieval are strongly encouraged to participate in the DisasterMM challenge. Industries and SMEs that develop similar AI technologies for semantic data fusion and retrieval of multi- or cross-lingual content are also warmly invited to participate. Moreover, the task could be of interest to researchers and practitioners in the domains of disaster management, emergency response, situational awareness, water management, and any other flood-related domains.
+
+#### Data
+The dataset for the RCTP subtask is a set of circa 8,000 social media posts collected from Twitter between May 25, 2020 and June 12, 2020, by searching for Italian keywords about floods (e.g. “alluvione”, “allagamento”, “esondazione” – all translated as flood). The ground truth of the dataset refers to the relevance of a tweet, i.e. 1 = relevant / 0 = not relevant.
+
+The dataset for the LETT subtask consists of circa 6,000 social media posts collected from Twitter between March 25, 2017 and August 1, 2018, again by searching for Italian, flood-related keywords.
+
+It should be also noted that only the IDs of the tweets will be distributed to the participants, in order to be fully compliant with the Twitter Developer Agreement & Policy. However, a tool to download them will be provided, while for the LETT subtask the clean, processed sentences will be also shared, for a fairer evaluation.
+
+#### Ground truth
+The ground truth of this dataset involves the following labels for each word of a tweet text: “B-LOC” for the first word of a sequence that refers to a location or a single-word location, “I-LOC” for the subsequent word of a sequence that refers to a location, and “O” for any non-location word. For instance, the ground truth for the sentence “Allagamento in via Prati della Farnesina” is “O O B-LOC I-LOC I-LOC I-LOC”.
+
+Both datasets have been manually annotated by native speakers that are employed by the Eastern Alps River Basin District, which is responsible for the hydrogeological defense and flood risk management in the Eastern Alps partition of North-East Italy.
+
+#### Evaluation methodology
+In RCTP, the evaluation metric for the binary classification of tweets as relevant (1) or not relevant (0) will be F1-score.
+
+In LETT, F1-score will be used too, not in sentence level, but in word level. To further explain, if a given label for a word matches the label of the annotator for this particular word, then it is considered as true (true positive if “B-LOC”/“I-LOC”, true negative if “O”). Two scores will be measured per each run: the exact F1-score, where labels have to fully match, and the partial F1-score, where either “B-LOC” or “I-LOC” can be considered as true as long as the annotator’s label concerns location.
+
+#### Quest for insight
+Here are several research questions related to this challenge that participants can strive to answer in order to go beyond just looking at the evaluation metrics:
+* What do the visual features contribute to the classification? Which properties of the images are important?
+* What metadata can be useful in relevance classification? Why?
+* What types of posts are misclassified as relevant to flooding incidents?
+* Is it easier to detect single-word or multi-word locations? Why?
+* What types of words are misrecognized as locations?
+* What additional challenges are met when analyzing Italian text, compared to English?
+
+#### Participant information
+Please contact your task organizers with any questions on these points.
+* Signing up: Fill in the [registration form](https://forms.gle/JcKoa5ycxR2KEiTJ7) and fill out and return the [usage agreement](https://multimediaeval.github.io/editions/2022/docs/MediaEval2022_UsageAgreement.pdf).
+* Making your submission: To be announced (check the task read me)
+* Preparing your working notes paper: Instructions on preparing you working notes paper can be found in [MediaEval 2022 Working Notes Paper Instructions](https://docs.google.com/document/d/12uSn0rRYxa3buiFNEbpa46dKsHOyqV2PHU_joRGMHRw).
+
+#### References and recommended reading
+[1] Andreadis, S., Gialampoukidis, I., Bozas, A., Moumtzidou, A., Fiorin, R., Lombardo, F., Karakostas, A., Norbiato, D., Vrochidis, S., Ferri,M., and Kompatsiaris, I., 2021, December. [WaterMM: Water Quality in Social Multimedia Task at MediaEval 2021](https://2021.multimediaeval.com/paper4.pdf). In Proceedings of the MediaEval 2021 Workshop, Online.
+
+[2] Andreadis, S., Antzoulatos, G., Mavropoulos, T., Giannakeris, P., Tzionis, G., Pantelidis, N., Ioannidis, K., Karakostas, A., Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I., 2021, May. [A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets](https://doi.org/10.1016/j.osnem.2021.100134). In Online Social Networks and Media Journal, Elsevier, Volume 23, pp. 100-134.
+
+[3] Andreadis, S., Gialampoukidis, I., Karakostas, A., Vrochidis, S., Kompatsiaris, I., Fiorin, R., Norbiato, D. and Ferri, M., 2020, December. [The flood-related multimedia task at mediaeval 2020](http://ceur-ws.org/Vol-2882/paper5.pdf). In Proceedings of the MediaEval 2020 Workshop, Online (pp. 14-15).
+
+[4] Moumtzidou, A., Andreadis, S., Gialampoukidis, I., Karakostas, A., Vrochidis, S. and Kompatsiaris, I., 2018, April. [Flood relevance estimation from visual and textual content in social media streams](https://dl.acm.org/doi/abs/10.1145/3184558.3191620). In Companion Proceedings of the The Web Conference 2018 (pp. 1621-1627).
+
+#### Task organizers
+* Lead task organizer: Stelios Andreadis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece, andreadisst@iti.gr
+* Aristeidis Bozas, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Ilias Gialampoukidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Thanassis Mavropoulos, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Anastasia Moumtzidou, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Stefanos Vrochidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Ioannis Kompatsiaris, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+* Roberto Fiorin, Eastern Alps River Basin District, Italy
+* Francesca Lombardo, Eastern Alps River Basin District, Italy
+* Daniele Norbiato, Eastern Alps River Basin District, Italy
+* Michele Ferri, Eastern Alps River Basin District, Italy
+
+#### Task Schedule
+* 22 July 2022: Development set release
+* 7 October 2022: Test set release
+* 11 November 2022: Runs due
+* 23 November 2022: Results returned
+* 28 November 2022: Working notes paper
+* 12-13 January 2023: 13th Annual MediaEval Workshop, Collocated with [MMM 2023](https://www.mmm2023.no/) in Bergen, Norway and also online.
+
diff --git a/_editions/2022/tasks/emotionalmario.md b/_editions/2022/tasks/emotionalmario.md
new file mode 100644
index 000000000..063d31231
--- /dev/null
+++ b/_editions/2022/tasks/emotionalmario.md
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+---
+# static info
+layout: task
+year: 2022
+hide: false
+
+# required info
+title: "Emotional Mario: A Game Analytics Challenge"
+subtitle:
+blurb: "Identify events of high significance in the Super Mario Bros. gameplay by analyzing facial expressions and the biometric data of players and then (optionally) creating a video summary of the best moments of play."
+---
+
+
+*See the [MediaEval 2022 webpage](https://multimediaeval.github.io/editions/2022/) for information on how to register and participate.*
+
+#### Task Description
+The EmotionalMario challenge focuses on the iconic Super Mario Bros. video game and provides a multimodal data set based on a Super Mario Bros. implementation for OpenAI Gym. The data set contains for multiple players their game input, demographics, biomedical sensory input from a medical-grade device, and videos of their faces while playing the game.
+
+Participants develop approaches to two subtasks:
+* *Event detection*: identify events of high significance in the gameplay by just analyzing the facial video and the biometric data. Such significant events include the end of a level, a power-up or extra life for Mario, or Mario’s death.
+* *Summarizing the gameplay* (optional): create a summary of the best moments of the play. There is no constraint on the modalities of the story, so it can be video, audio, text, images, or a combination. The summary can include gameplay scenes, facial video, data visualization, and whatever comes to your mind for helping such a summary.
+
+#### Motivation and background
+With the rise of deep learning, many large leaps in research have been achieved in recent years such as human-level image recognition, text classification, and even content creation. Games and deep learning also have a rather long history together, specifically in the context of reinforcement learning. However, video games still pose a lot of challenges. Games are understood as engines of experience [1], and as such, they need to invoke human emotions. While emotion recognition has come a far way over the last decade [2], the connection between emotions and video games is still an open and interesting research question.
+
+As games are designed to evoke emotions [1], we hypothesize that emotions in the player are reflected in the visuals of the video game. Simple examples are when players are happy after having mastered a particularly complicated challenge, when they are shocked by a jump scare scene in a horror game, or when they are excited after unlocking a new resource. These things can be measured by questionnaires after playing [3], but in the Emotional Mario task, we want to interconnect emotions and gameplay based on data instead of asking the players.
+
+
+#### Target group
+The target group for this task is diverse and broad. It includes researchers and practitioners from game design and development, game studies, machine learning, data science, artificial intelligence, and interactive multimedia. We also encourage interdisciplinary research involving people from psychology, game studies, and the humanities discussing the interrelation of biometric data, facial expressions, and gameplay. In any case, regardless of the research background, the submission will help to have a basic understanding of how we can better understand the connection between gameplay and the reaction of the player.
+
+#### Data
+For the task, we provide Toadstool [4], a data set gathered from ten participants playing Super Mario Bros. Based on the protocols established in [4] we extend the data set by ten more participants. We gathered gameplay, video, and sensor data while people played Super Mario Bros. Data includes for instance heart rate, skin conductivity, videos of the players’ faces synchronized to the gameplay, but also the gameplay itself, demographics on the players and their scores and times spent in the game. For the Emotional Mario task (i) we release a training set including the original Toadstool data and new data on some additional participants (ii) an additional four participants will serve as ground truth and are to be published after the evaluation of the submitted runs.
+
+#### Evaluation methodology
+* *Event detection*: We will focus on precision and recall for finding the events within the gameplay. These events include player deaths, obtaining power-ups, and completing a level. We will provide ground truth for the events for training and will also provide an evaluation script that allows self-evaluation based on the training data.
+* *Summarizing the gameplay* Evaluation for the second task will be jury-based. The jury includes An expert panel with professionals and researchers from the field of game development, game studies, e-sports, and media sciences. Judges will be presented with the summary videos and will judge them on:
+
+** Informative value (i.e. is it a good summary of the gameplay),
+** Accuracy (i.e. does it reflect the emotional up and downs and the skill of the play), and
+** Innovation (ie. surprisingly new approach, non-linearity of the story, creative use of cuts, etc.)
+
+
+#### Quest for insight
+Here are several research questions related to this challenge that participants can strive to answer in order to go beyond just looking at the evaluation metrics:
+* Which events do you expect a priori to be easiest or most difficult to detect (based on your assumption of the emotional impact of these events). Do the results of the Event detection subtask fit your expectations?
+* Do you anticipate consistency accross players with respect to their emotional reaction? Why are why not? What are the implications of personal emotional reactions for Event detection?
+* Which elements would you like to ideally include in a summary? Why are these elements easy or difficult to extract automatically from the data?
+
+#### Participant information
+Please contact your task organizers with any questions on these points.
+* Signing up: Fill in the [registration form](https://forms.gle/JcKoa5ycxR2KEiTJ7) and fill out and return the [usage agreement](https://multimediaeval.github.io/editions/2022/docs/MediaEval2022_UsageAgreement.pdf).
+* Making your submission: To be announced (check the task read me)
+* Preparing your working notes paper: Instructions on preparing you working notes paper can be found in [MediaEval 2022 Working Notes Paper Instructions](https://docs.google.com/document/d/12uSn0rRYxa3buiFNEbpa46dKsHOyqV2PHU_joRGMHRw).
+
+#### References and recommended reading
+[1] Sylvester, T. (2013). Designing games: A guide to engineering experiences. " O'Reilly Media, Inc.".
+
+[2] Saxena, Anvita, Ashish Khanna, and Deepak Gupta. "Emotion recognition and detection methods: A comprehensive survey." Journal of Artificial Intelligence and Systems 2.1 (2020): 53-79.
+
+[3] Abeele, V. V., Spiel, K., Nacke, L., Johnson, D., & Gerling, K. (2020). Development and validation of the player experience inventory: A scale to measure player experiences at the level of functional and psychosocial consequences. International Journal of Human-Computer Studies, 135, 102370.
+
+[4] Svoren, H., Thambawita, V., Halvorsen, P., Jakobsen, P., Ceja, E. G., Noori, F. M., … Hicks, S. (2020, February 28). Toadstool: A Dataset for Training Emotional Intelligent Machines Playing Super Mario Bros. https://doi.org/10.31219/osf.io/4v9mp
+
+#### Task organizers
+* Mathias Lux, Mu'taz Alshaer (Alpen-Adria-Universität Klagenfurt, AT)
+* Michael Riegler, Pål Halvorsen, Vajira Thambawita, and Steven Hicks (SimulaMet Oslo, NO)
+* Duc-Tien Dang-Nguyen (University of Bergen, NO)
+
+#### Task Schedule
+* July-August 2022: Data release
+
+* November 2022: Runs due and results returned. Exact dates to be announced.
+
+
+* 28 November 2022: Working notes paper
+* 12-13 January 2023: 13th Annual MediaEval Workshop, Collocated with [MMM 2023](https://www.mmm2023.no/) in Bergen, Norway and also online.
diff --git a/_editions/2022/tasks/fakenews.md b/_editions/2022/tasks/fakenews.md
new file mode 100644
index 000000000..a1f02e4ca
--- /dev/null
+++ b/_editions/2022/tasks/fakenews.md
@@ -0,0 +1,104 @@
+---
+# static info
+layout: task
+year: 2022
+hide: false
+
+# required info
+title: "FakeNews Detection"
+subtitle:
+blurb: "Participants address three fake news detection subtasks related to COVID-19-related conspiracy theories on twitter: First, text-based topic and conspiracy detection, second, graph based detection of users who post conspiracy theory (posters) in a social network graph with node attributes, and, third, a combination the two to achieve topic and conspiracy detection based both textual data and graphs."
+---
+
+
+*See the [MediaEval 2022 webpage](https://multimediaeval.github.io/editions/2022/) for information on how to register and participate.*
+
+#### Task Description
+The FakeNews Detection Task offers three fake news detection subtasks on COVID-19-related conspiracy theories. The first subtask includes text-based topics and conspiracy detection. The second subtask asks for graph based detection of users who post conspiracy theory (posters) in a social network graph with node attributes. The third subtasks combine the two, aiming at topic and conspiracy detection based on both textual data and graphs.
+
+All subtasks are related to misinformation disseminated in the context of the COVID-19 pandemic. We focus on conspiracy theories that purport some kind of nefarious actions by governments or other actors related to CODID-19, such as intentionally spreading the pandemic, lying about the nature of the pandemic, or using vaccines that have some hidden functionality and purpose.
+
+* *Subtask 1: Text-Based Misinformation and Conspiracies Detection:* In this subtask, the participants receive a dataset consisting of tweet text blocks in English related to COVID-19 and various conspiracy theories. The goal of this subtask is to build a complex multi-labelling multi-class detector that for each topic from a list of predefined conspiracy topics can predict whether a tweet promotes/supports or just discusses that particular topic. This task is identical to a task posed in last year’s challenge, but it uses a larger development and test datasets.
+
+* *Subtask 2: Graph-Based Conspiracy Source Detection:* In this subtask, the participants are given an undirected graph derived from social network data where the vertices are users and the edges represent connections between them. Each vertex has a set of attributes, including location, number of followers, as well as some texts posted by that user. Some users are labeled as misinformation posters, based on manually annotated tweets, and some are labeled as non-misinformation posters. This subtask asks participants to classify the other users in the graph, based on their connection to the labeled users as well as their attributes. Scoring will be based on correctly classifying users/vertices in the graph that have manually generated hidden labels.
+
+* *Subtask 3: Graph and Text-Based Conspiracy Detection:* This subtask combines the data of both previous subtasks with the aim of improving the text-based classification. For each text to be evaluated, the vertex corresponding to the author is specified in the graph. The goal of this subtask is the same as that of Subtask 1, but participants can make full use of the graph data and vertex attributes. This subtask will use the same development and a different test set from that of Subtask 1.
+
+#### Motivation and background
+Digital wildfires, i.e., fast-spreading inaccurate, counterfactual, or intentionally misleading information, can quickly permeate public consciousness and have severe real-world implications, and they are among the top global risks in the 21st century. While a sheer endless amount of misinformation exists on the internet, only a small fraction of it spreads far and affects people to a degree where they commit harmful and/or criminal acts in the real world. The COVID-19 pandemic has severely affected people worldwide, and consequently, it has dominated world news for months. Thus, it is no surprise that it has also been the topic of a massive amount of misinformation, which was most likely amplified by the fact that many details about the virus were unknown at the start of the pandemic. This task aims at the development of methods capable of detecting such misinformation. Since many different misinformation narratives exist, such methods must be capable of distinguishing between them. For that reason we consider a variety of well-known conspiracy theories related to COVID-19.
+
+#### Target group
+The task is of interest to researchers in the areas of online news, social media, multimedia analysis, multimedia information retrieval, natural language processing, and meaning understanding and situational awareness to participate in the challenge. The target knowledge areas include Machine and Deep Learning, Natural Language Processing and Graphs Analysis Algorithms.
+
+#### Data
+The datasets contain several sets of tweet texts mentioning Corona Virus and different conspiracy theories and corresponding undirected graphs derived from social network data where the vertices are users and the edges represent connections between them. The tweet-text sets consist of only English language posts and they contain a variety of long tweets with neutral, positive, negative, and sarcastic phrasing. The vertices of tweet-graphs contain a set of user attributes as well as some texts posted by that user. The datasets are not balanced with respect to the number of samples of conspiracy-promoting and other tweets, the number of tweets per conspiracy class, or the graph structures. The dataset items have been collected from Twitter during a period between 20th of January 2020 and 1st of April 2022, by searching for the Corona-virus-related keywords (e.g., “corona”, “COVID-19”, etc.) in the tweets’ text, followed by a search for keywords related to the conspiracy theories. Since not all tweets are available online, the participants will be provided a full-text set of already downloaded tweets. In order to be compliant with the Twitter Developer Policy, only the members of the participants’ participating teams are allowed to access and use the provided dataset. Distribution, publication, sharing and any form of usage of the provided data apart from the research purposes within the FakeNews task is strictly prohibited. A copy of the dataset in form of Tweet ID and annotations will be published after the end of MediaEval 2022.
+
+#### Ground truth
+The ground truth for the provided dataset was created by the team of well-motivated students and researchers using an overlapping annotation process with the following cross-validation and verification by an independent assisting team.
+
+#### Evaluation methodology
+Evaluation will be performed using standard implementation of the multi-class generalization of the Matthews Correlation Coefficient (MCC, https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html) computed on the optimally threshold conspiracy promoting probabilities (threshold that yields the best MCC score).
+
+#### Quest for insight
+Here are several research questions related to this challenge that participants can strive to answer in order to go beyond just looking at the evaluation metrics:
+* Which properties of tweet texts (word use, style, complexity) correlate with a tweet being labeled as related to a conspiracy?
+* Are these properties stable over time and across posters? A priori would there be reasons to expect them to be?
+* Which attributes in the graph correlate with a tweet being labeled as related to a conspiracy?
+
+#### Participant information
+Please contact your task organizers with any questions on these points.
+* Signing up: Fill in the [registration form](https://forms.gle/JcKoa5ycxR2KEiTJ7) and fill out and return the [usage agreement](https://multimediaeval.github.io/editions/2022/docs/MediaEval2022_UsageAgreement.pdf).
+* Making your submission: To be announced (check the task read me)
+* Preparing your working notes paper: Instructions on preparing you working notes paper can be found in [MediaEval 2022 Working Notes Paper Instructions](https://docs.google.com/document/d/12uSn0rRYxa3buiFNEbpa46dKsHOyqV2PHU_joRGMHRw).
+
+#### References and recommended reading
+##### General
+[1] Nyhan, Brendan, and Jason Reifler. 2015. Displacing misinformation about events: An experimental test of causal corrections. Journal of Experimental Political Science 2, no. 1, 81-93.
+
+##### Twitter data collection and analysis
+[2] Burchard, Luk, Daniel Thilo Schroeder, Konstantin Pogorelov, Soeren Becker, Emily Dietrich, Petra Filkukova, and Johannes Langguth. 2020. A Scalable System for Bundling Online Social Network Mining Research. In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 1-6.
+
+[3] Schroeder, Daniel Thilo, Konstantin Pogorelov, and Johannes Langguth. 2019. FACT: a Framework for Analysis and Capture of Twitter Graphs. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 134-141.
+
+[4] Achrekar, Harshavardhan, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, and Benyuan Liu. 2011. Predicting flu trends using twitter data. In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, 702-707.
+
+[5] Chen, Emily, Kristina Lerman, and Emilio Ferrara. 2020. Covid-19: The first public coronavirus Twitter dataset. arXiv preprint arXiv:2003.07372.
+
+[6] Kouzy, Ramez, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus 12, no. 3.
+
+##### Natural language processing
+[7] Bourgonje, Peter, Julian Moreno Schneider, and Georg Rehm. 2017. From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, 84-89.
+
+[8] Imran, Muhammad, Prasenjit Mitra, and Carlos Castillo. 2016. Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages. arXiv preprint arXiv:1605.05894.
+
+##### Information spreading
+[9] Liu, Chuang, Xiu-Xiu Zhan, Zi-Ke Zhang, Gui-Quan Sun, and Pak Ming Hui. 2015. How events determine spreading patterns: information transmission via internal and external influences on social networks. New Journal of Physics 17, no. 11.
+
+##### Online news sources analysis
+[10] Pogorelov, Konstantin, Daniel Thilo Schroeder, Petra Filkukova, and Johannes Langguth. 2020. A System for High Performance Mining on GDELT Data. In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE, 1101-1111.
+
+##### Past Experience
+See the [MediaEval 2020](http://ceur-ws.org/Vol-2882) and MediaEval 2021 Working Notes Proceedings.
+
+[11] de Rijk, Lynn. 2020. You Said It? How Mis- and Disinformation Tweets Surrounding the Corona-5G-Conspiracy Communicate Through Implying. Working Notes Proceedings of the MediaEval 2020 Workshop. [http://ceur-ws.org/Vol-2882/paper58.pdf](http://ceur-ws.org/Vol-2882/paper58.pdf)
+
+#### Task organizers
+* Konstantin Pogorelov, Simula Research laboratory (Simula), Norway, konstantin (at) simula.no
+* Johannes Langguth, Simula Research laboratory (Simula), Norway, langguth (at) simula.no
+* Daniel Thilo Schroeder, Simula Research laboratory (Simula), Norway
+
+#### Task auxiliaries
+* Özlem Özgöbek, Norwegian University of Science and Technology (NTNU), Norway
+
+#### Task Schedule
+* 30 August 2022: Data release
+
+* 21 November 2022 AOE: Runs submission due.
+* 22 November 2022: Runs evaluation results returned.
+
+
+* 01 December 2022 AOE: Working notes paper
+* 12-13 January 2023: 13th Annual MediaEval Workshop, Collocated with [MMM 2023](https://www.mmm2023.no/) in Bergen, Norway and also online.
+
+#### Acknolwedgments
+This work was funded by the Norwegian Research Council under contracts #272019 and #303404 and has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract #270053. We also acknowledge support from Michael Kreil in the collection of Twitter data.
diff --git a/_editions/2022/tasks/medico.md b/_editions/2022/tasks/medico.md
new file mode 100644
index 000000000..21b8063e1
--- /dev/null
+++ b/_editions/2022/tasks/medico.md
@@ -0,0 +1,191 @@
+---
+# static info
+layout: task
+year: 2022
+hide: false
+
+# required info
+title: "Medical Multimedia Task: Transparent Tracking of Spermatozoa"
+subtitle:
+blurb: "Detect and track spermatozoa in medical video, with the goal to create a real-time system. Calculate/predict attributes such as speed and travel distance."
+---
+
+
+*See the [MediaEval 2022 webpage](https://multimediaeval.github.io/editions/2022/) for information on how to register and participate.*
+
+#### Task Description
+The 2022 Medico task tackles the challenge of tracking sperm cells of video recordings of spermatozoa. The development dataset contains 20 videos, each one is 30 seconds long, a set of sperm characteristics (hormones, fatty acids data, etc.), frame-by-frame bounding box annotations, some anonymized study participants-related data, and motility and morphology data following the WHO guidelines. The goal is to encourage task participants to track individual sperms in real-time and combine different data sources to predict common measurements used for sperm quality assessment, specifically the motility (movement) spermatozoa (living sperm).
+
+We hope that this task will encourage the multimedia community to aid in the development of computer-assisted reproductive health and discover new and clever ways of analyzing multimodal datasets. In addition to good analysis performance, an important aspect is also the efficiency of the algorithms due to the fact that the assessment of the sperm is performed in real-time and therefore requires real-time feedback.
+
+For the task, we will provide a dataset of videos and other data from 20 different patients. Based on this data, the participants will be asked to address the following four subtasks:
+
+* *Subtask 1: Sperm cell tracking* is real-time tracking of sperm cells in a given sperm videos. Tracking should be performed by predicting bounding box coordinates with the similar format to the bounding box coordinates provided with the development datasets. In this task, models should track sperm in each frame of a provided video in real-time. Therefore, frames per second is a important factor to measure.
+
+* *Subtask 2: Prediction of motility* in terms of the percentage of progressive and non-progressive spermatozoa is the second task. The prediction needs to be performed sample wise resulting in one value per sample per predicted attribute. Sperm tracking or bounding boxes predicted in the task 1 are required to use to solve the task. Motility is the ability of an organism to move independently, and where a progressive spermatozoon is able to "move forward", a non-progressive would move in circles without any forward progression.
+
+* *Subtask 3: Catch and highlight* task focus on identifying fastest sperm cells with corresponding average speed and highest top speed. One specific challenge with this subtask is that the video also changes the view on the sample. This happens because the sample is moved below the microscope to observe the complete sample area. Therefore, the tracking has to be performed per viewpoint on the sample. (Optional Subtask.)
+
+* *Subtask 4: Explainability of predicitons* is perfomed in Subtasks 1 and/or 2 and/or 3 should be explained using machine learning explainable methods to convince domain experts about the final outputs. There is no any specific pre-requirements for this task. However, a report should be provided with explainable methods and corresponding results. (Optional Subtask.)
+
+For both Subtasks 2 and 3, task-participants are asked to perform video analysis over single frame analysis. This is important due to the fact that single frame-based analysis will not be able to catch the movement of the spermatozoa (motility) which contains important information to perform the predictions on Subtasks 2 and 3.
+
+
+#### Motivation and background
+Manual evaluation of a sperm sample using a microscope is time-consuming and requires costly experts who have extensive training. In addition, the validity of manual sperm analysis becomes unreliable due to limited reproducibility and high inter-personnel variations due to the complexity of tracking, identifying, and counting sperms in fresh samples. The existing computer-aided sperm analyzer systems are not working well enough for application in a real clinical setting due to unreliability caused by the consistency of the semen sample. Therefore, we need to research new methods for automated sperm analysis.
+
+
+#### Target group
+The task is of interest to researchers in the areas of machine learning (classification), visual content analysis and multimodal fusion. Overall, this task is intended to encourage the multimedia community to help improve the health care system through application of their knowledge and methods to reach the next level of computer and multimedia assisted diagnosis, detection and interpretation.
+
+#### Data
+The task uses the data set VISEM [2], which contains data from 85 male participants aged 18 years or older. For this task, we have selected only 30 seconds video clips from selected 20 videos. For each participant, we include a set of measurements from a standard semen analysis, a video of live spermatozoa, a sperm fatty acid profile, the fatty acid composition of serum phospholipids, study participants-related data, and WHO analysis data. The dataset contains 20 videos, with each video has 30 seconds duration with corresponding bounding box coordinates. Each video has a resolution of 640x480 and runs at 50 frames-per-second. The dataset contains in total six CSV files (five for data and one which maps video IDs to study participants' IDs), a description file, and folders containing the videos and bounding box data. The name of each video file contains the video's ID, the date it was recorded, and a small optional description. Then, the end of the filename contains the code of the person who assessed the video. Furthermore, VISEM contains five CSV files for each of the other data provided, a CSV file with the IDs linked to each video, and a text file containing * descriptions of some of the columns of the CSV files. One row in each CSV file represents a participant. The provided CSV files are:
+* semen_analysis_data: The results of standard semen analysis.
+* fatty_acids_spermatozoa: The levels of several fatty acids in the spermatozoa of the participants.
+* fatty_acids_serum: The serum levels of the fatty acids of the phospholipids (measured from the blood of the participant).
+* sex_hormones: The serum levels of sex hormones measured in the blood of the participants.
+* study_participant_related_data: General information about the participants such as age, abstinence time, and Body Mass Index (BMI).
+* videos: Overview of which video file belongs to what participant.
+
+All Study participants agreed to donate their data for the purpose of science and provided the necessary consent for us to be able to distribute the data (checked and approved by the Norwegian data authority and ethical committee).
+
+##### Development data is available now:
+* [Data download link 1 - Kaggle](https://www.kaggle.com/datasets/vlbthambawita/visemtracking)
+* [Data download link 2 - Simula-dataset](https://datasets.simula.no/visem-tracking/)
+
+
+#### Ground truth
+The ground truth data provided in this task were prepared by expert computer scientists and verified by domain experts.
+
+#### Evaluation methodology
+For the evaluation, we will use mAP (mean average precision), mean squared error, mean absolute error, frames per seconds and the mean absolute percentage error for the first two subtasks. For the optional third and fourth task, we will use manual evaluation with the help of three different experts within human reproduction.
+
+### Test data downlod link
+The prediction of this test dataset should be uploaded using the following submission form.
+[Test data download link](https://www.dropbox.com/sh/2ohitza5ouzh2d3/AAD_8VnvdhPqOVlCcAn21Uc8a?dl=0)
+
+### Submission instructions
+
+[Submission form](https://forms.gle/Bgwt5pEwwKm6HPH26)
+
+#### Sub-task 1:
+
+If you are interested in submitting only for detecting sperm in individual frames, then your submission file should be matched to the provided ground truth format (YOLO format). You have to follow the similar file structure of the dataset. Check the folder structure in [https://www.kaggle.com/datasets/vlbthambawita/visemtracking](https://www.kaggle.com/datasets/vlbthambawita/visemtracking). A sample .txt file is below.
+
+```
+source_code
+ |- code_and_checkpoints
+ |- README.txt (must explain how to run your model to detect sperms on a new video)
+ |- run.sh (shell script file to run your models for new video inputs (.mp4))
+predictions
+ |-
+ |- labels
+ |-
+
+
+
+
+
+### Schedule
+* End of July 2020: First data releases
+* Mid November 2020: Runs due (See individual task pages for the exact deadlines)
+* 30 November 2020: Working notes paper
+* 11,14-15 December 2020: MediaEval 2020 Workshop (Fully online.)
+
+#### The MediaEval Organization
+MediaEval is made possible by the efforts of a larger number of task organizers, who each are responsible for organizing their own tasks. Please see the individual task pages for their name. The over all coordination is carried out by the MediaEval Logistics Committe and guided by the Community Council.
+
+##### The MediaEval Logistics Committee (2020)
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Steven Hicks, SimulaMet, Norway
+* Ngoc-Thanh Nguyen, University of Information Technology, Vietnam
+* Ricardo Manhães Savii, Dafiti Group, Brasil
+* With special thanks to Bart Thomee, Verily, US
+
+##### The MediaEval Community Council (2020)
+* Martha Larson, Radboud University, Netherlands (Coordinator and contact person)
+* Gareth J. F. Jones, Dublin City University, Dublin, Ireland
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+
+
+
+MediaEval is grateful for the support of [ACM Special Interest Group on Multimedia](http://sigmm.org/)
+
+
+
+For more information, contact m.larson (at) ru.cs.nl. You can also follow us on Twitter @multimediaeval
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+
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+This folder contains `Markdown` (.md) files to all tasks for 2020 MediaEval edition.
+
+## How to edit
+
+Opening a file and clicking on the pencil logo (view Figure 1 below)
+
+
+
+you will access `edit` mode on the file aand you will see something like below:
+
+
+
+There are 2 main parts to the document:
+
+* part 1 (lines 1 to 11) is the task file metadata. Here, you (task organizer), should fill in all `# required info` fields (title, subtitle, and blurb). When your task content is ready to be published on the website, to be shown on the website, then you should edit the `hide` property to `false`, this way your task will be visible on the website.
+
+* part 2 (lines 12 to infinity) is the actual task content. There is a suggested structure to the document to be followed. This part accepts content with [Markdown](https://daringfireball.net/projects/markdown/syntax) and HTML syntax.
+
+After you fill all content `commit changes` by filling the form below that edit screen and clicking on `Propose changes` as shown in Figure 3 below:
+
+
+
+That action will open a new window in which you will confirm a `Pull request`. As you can see in Figure 4 below:
+* yellow arrow points out where you can select a reviewer (if you are already talking to one of the website admins), this is optional
+* fill your comments on the `fill here` space as you believe it's required to support approval of your change.
+* red arrow points to the button that confirms your `Pull request`
+
+
+
+Other than that please feel free to ask for help. This structure is and experiment and we need help to turn it useful and easy to everyone. MediaEval organizers are available to help or submit questions and issues [here](https://github.com/multimediaeval/multimediaeval.github.io/issues).
+
diff --git a/_editions/2020/tasks/fakenews.md b/_editions/2020/tasks/fakenews.md
index a190d3d9f..a62a30b35 100644
--- a/_editions/2020/tasks/fakenews.md
+++ b/_editions/2020/tasks/fakenews.md
@@ -2,61 +2,85 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title: FakeNews Task
-subtitle: subtitle
-blurb: This is the blurb for fakenews task
+title: "FakeNews: Corona virus and 5G conspiracy"
+subtitle: Corona virus and 5G conspiracy
+blurb: "Spontaneous and intentional digital Fake News wildfires over on-line social media can be as dangerous as natural fires. A new generation of data mining and analysis algorithms is required for early detection and tracking of information waves. This task focuses on the analysis of tweets around Coronavirus and 5G conspiracy theories in order to detect misinformation spreaders."
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+The Fake News Detection Task 2020 offers two Fake News Detection subtasks on COVID-19 and 5G conspiracy topics. The first subtask includes NLP-based Fake News Detection while the second subtask targets the detection of abnormal spreading patterns. Both tasks are related to misinformation disseminated in the context of the COVID-19 crisis. More specifically, misinformation claims that the construction of the 5G network and the associated electromagnetic radiation trigger the SARS-CoV-2 virus.
-#### Introduction
+***NLP-Based Fake News Detection***: In this subtask, the participants receive a dataset, including 10.000 Tweets in English related to COVID-19 and 5G and their corresponding metadata. The participants are encouraged to build a binary classifier that can predict whether a Tweet contains the disinformation described above or whether it only accidentally contains the two buzzwords.
+***Structure-Based Fake News Detection***: Here, the participants receive a data record with German tweets; some of these tweets are also related to the false information described above. Although the content of the tweets is automatically translated into English, it is only secondary because each tweet contains a distribution graph in addition to the text. A distribution graph here is the subgraph of the Twitter follower network, which contains the nodes (Twitter users) who shared the respective tweet.
-#### New for 2020
+#### Motivation and Background
+Digital wildfires, i.e., fast-spreading inaccurate, counterfactual, or intentionally misleading information, can quickly permeate public consciousness and have severe real-world implications and are among the top global risks in the 21st century. While a sheer endless amount of misinformation exists on the internet, only a small fraction of it spreads far and affects people to a degree where they commit harmful and/or criminal acts in the real world. The COVID-19 pandemic has severely affected people worldwide, and consequently, it has dominated world news for months. Thus, it is no surprise that it has also been the topic of a massive amount of misinformation, which was most likely amplified by the fact that many details about the virus were unknown at the start of the pandemic. We focus on specific misinformation - *the idea that the COVID-19 outbreak is somehow connected to the introduction of the 5G wireless technology*.
-#### Target group
+#### Target Group
+The task is of interest to researchers in the areas of online news, social media, multimedia analysis, multimedia information retrieval, natural language processing, and meaning understanding and situational awareness to participate in the challenge.
#### Data
-
-
-#### Ground Truth
+The dataset contains two sets of tweets mentioning Corona Virus and 5G that include text, reposting time patterns, and some basic info about reposters. The first set consists of only English language posts. The second set consists of translated tweets from German Twitter accounts only. The datasets are balanced with respect to the number of samples of different classes and variations of the amount of data that accompanies each tweet. We provide a wide variety of short, medium, and long tweets with neutral, positive, negative, and sarcastic phrasing.
#### Evaluation Methodology
+The ground truth is created by manual annotation of the collected tweets based on text-only analysis by a group of experts and volunteers. The main metric will be ROC AUC, but the task may explore other metrics as well.
#### References and recommended reading
+***General***
+
+[1] Nyhan, Brendan, and Jason Reifler. 2015. [Displacing misinformation about events: An experimental test of causal corrections](https://www.cambridge.org/core/journals/journal-of-experimental-political-science/article/displacing-misinformation-about-events-an-experimental-test-of-causal-corrections/69550AB61F4E3F7C2CD03532FC740D05#). Journal of Experimental Political Science 2, no. 1, 81-93.
+
+***Twitter data collection and analysis***
-### Big Picture of the Task
+[2] Schroeder, Daniel Thilo, Konstantin Pogorelov, and Johannes Langguth. 2019. [FACT: a Framework for Analysis and Capture of Twitter Graphs](https://ieeexplore.ieee.org/document/8931870). In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 134-141.
-#### Innovation
+[3] Achrekar, Harshavardhan, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, and Benyuan Liu. 2011. [Predicting flu trends using twitter data](https://ieeexplore.ieee.org/document/5928903). In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, 702-707.
+[4] Chen, Emily, Kristina Lerman, and Emilio Ferrara. 2020. [Covid-19: The first public coronavirus twitter dataset](https://arxiv.org/abs/2003.07372v1?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+CoronavirusArXiv+%28Coronavirus+Research+at+ArXiv%29). arXiv preprint arXiv:2003.07372.
-#### Focus
+[5] Kouzy, Ramez, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. [Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152572/). Cureus 12, no. 3.
+***Natural language processing***
-#### Risk management
+[6] Bourgonje, Peter, Julian Moreno Schneider, and Georg Rehm. 2017. [From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles](https://www.aclweb.org/anthology/W17-4215/). In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, 84-89.
+[7] Imran, Muhammad, Prasenjit Mitra, and Carlos Castillo. 2016. [Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages](https://arxiv.org/abs/1605.05894). arXiv preprint arXiv:1605.05894.
-#### Task organization team
+***Information spreading***
+[8] Liu, Chuang, Xiu-Xiu Zhan, Zi-Ke Zhang, Gui-Quan Sun, and Pak Ming Hui. 2015. [How events determine spreading patterns: information transmission via internal and external influences on social networks](https://iopscience.iop.org/article/10.1088/1367-2630/17/11/113045/pdf). New Journal of Physics 17, no. 11.
-#### Task organizers
+
+
-#### Task auxiliaries
+
+#### Task Organizers
+* Konstantin Pogorelov, Simula Research laboratory (Simula), Norway, konstantin (at) simula.no
+* Johannes Langguth, Simula Research laboratory (Simula), Norway, langguth (at) simula.no
+* Daniel Thilo Schroeder, Simula Research laboratory (Simula), Norway
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 10 August: Data release
+* ~~31 October~~ 16 November: Runs due + Start working on Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December : MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
+
diff --git a/_editions/2020/tasks/floodmultimedia.md b/_editions/2020/tasks/floodmultimedia.md
new file mode 100644
index 000000000..06b00059e
--- /dev/null
+++ b/_editions/2020/tasks/floodmultimedia.md
@@ -0,0 +1,91 @@
+---
+# static info
+layout: task
+year: 2020
+hide: false
+
+# required info
+title: Flood-related Multimedia
+subtitle: Flood classification in social multimedia for Northeastern Italy
+blurb: Floods are one of the most common natural disasters that occur on our planet, and the destruction they cause is enormous. In this task, the participants receive a set of Twitter posts (tweets), including text, images, and other metadata, and are asked to automatically identify which posts are truly relevant to flooding incidents in the specific area of Northeastern Italy. The ground truth labels have been created by experts in flood risk management. The ultimate aim of this task is to develop technology that will support experts in flood disaster management.
+---
+
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
+
+#### Task Description
+The Flood-related Multimedia Task tackles the analysis of social multimedia from Twitter for flooding events. In this task, the participants receive a set of Twitter posts (tweets) and their associated images, which contain keywords related to floods in a specific area of interest, specifically, the Eastern Alps partition in Northeastern (NE) Italy. However, the relevance of the tweets to actual flooding incidents in that area is ambiguous. The objective of the task is to build an information retrieval system or a classifier that is able to distinguish whether or not a tweet is relevant to a flooding event in the examined area.
+
+The dataset of the task consists of Italian-language tweets, motivated by the common flood events in the cities of Eastern Alps (e.g., Venice, Vicenza, Trieste) and surrounding areas. Participants can tackle the task using text features, image features, or a combination of both. We choose Italian for this task in order to encourage researchers to move away from a focus on English.
+
+
+
+*The area of interest: Eastern Alps partition in North-East Italy*
+
+#### Motivation and Background
+[Floods](https://www.nationalgeographic.com/environment/natural-disasters/floods/) are a natural disaster that affects most places on Earth and causes a vast number of deaths and damages. Citizen crowdsourcing and social media posts have been proven valuable in all stages of managing such a disaster: a) they can notify about a possible disaster in the pre-emergency phase; b) they can provide insights on the evolution of the incident and detect areas in danger during the disaster; and c) they can assist in the damage control in the post-emergency phase.
+
+However, the large and continuous streams of published posts carry a lot of noise (e.g., the metaphorical use of flood-related words, incidents outside the area of interest, past events), which makes it difficult to collect high-quality information. Automatic estimation of a tweet’s relevance could address this challenge, by filtering out unrelated posts. Better ability to separate relevant and not relevant tweets will contribute to improving the quality of the incoming information available to support first responders and civil protection authorities.
+
+#### Target Group
+Researchers in the areas of social media, multimedia and multilingual analysis, multimedia classification and information retrieval are strongly encouraged to participate in the challenge. Industries and SMEs that develop similar AI technologies for semantic data fusion and retrieval of multi- or cross-lingual content are also warmly invited to participate. In addition, the task could be of interest to researchers and practitioners in the domains of disaster management, emergency response, situational awareness, water management, and any other flood-related domains.
+
+#### Data
+The dataset is a list of social media posts that have been collected from Twitter between 2017 and 2019, by searching for Italian flood-related keywords (e.g., “allagamento”, “alluvione”) inside the tweet text. All tweets contain an attached image and should be still online at the time of releasing the dataset. In order to be compliant with the Twitter Developer Policy, only the IDs of the tweets can be distributed, but a tool to download them will also be provided.
+
+The ground truth of the dataset has been collected with human annotation, by the Alto Adriatico Water Authority (AAWA), who are experts on flood risk management in the Eastern Alps partition of NE Italy. It should be noted here that only tweets that refer to floods in this specific region have been annotated as relevant.
+
+#### Evaluation Methodology
+The evaluation metric for the binary classification of tweets as relevant (1) or not relevant (0) will be F-score.
+
+#### References and recommended reading
+
+
+[1] Moumtzidou, A., Andreadis, S., Gialampoukidis, I., Karakostas, A., Vrochidis, S. and Kompatsiaris, I., 2018, April. [Flood relevance estimation from visual and textual content in social media streams](https://dl.acm.org/doi/abs/10.1145/3184558.3191620). In Companion Proceedings of the The Web Conference 2018 (pp. 1621-1627).
+
+[2] Peters, R. and de Albuquerque, J.P., 2015. [Investigating images as indicators for relevant social media messages in disaster management](). In ISCRAM.
+
+[3] Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C., Squicciarini, A.C. and Tapia, A.H., 2015, May. [Twitter Mining for Disaster Response: A Domain Adaptation Approach](http://www.agora.icmc.usp.br/site/wp-content/uploads/2015/08/Peters-and-Albuquerque-2015-Investigating-images-as-indicators-for-relevant-social-media-messages-in-disaster-management.pdf). In ISCRAM.
+
+[4] Imran, M., Castillo, C., Lucas, J., Meier, P. and Vieweg, S., 2014, April. [AIDR: Artificial intelligence for disaster response](https://dl.acm.org/doi/abs/10.1145/2567948.2577034). In Proceedings of the 23rd International Conference on World Wide Web (pp. 159-162).
+
+[5] Brouwer, T., Eilander, D., Van Loenen, A., Booij, M.J., Wijnberg, K.M., Verkade, J.S. and Wagemaker, J., 2017. [Probabilistic flood extent estimates from social media flood observations](https://core.ac.uk/download/pdf/207400745.pdf). Natural Hazards & Earth System Sciences, 17(5).
+
+
+We also recommend to read past years’ task papers in the MediaEval Proceedings:
+
+[1] Proceedings of the MediaEval 2019 Workshop.
+
+[2] Martha Larson, et al. (eds.) 2018. [Proceedings of the MediaEval 2018 Workshop](http://ceur-ws.org/Vol-2283/), Sophia Antipolis, France, Oct. 29-31, 2018.
+
+[3] Guillaume Gravier et al. (eds.) 2017. [Proceedings of the MediaEval 2017 Workshop](http://ceur-ws.org/Vol-1984), Dublin, Ireland, Sept. 13-15, 2017.
+
+
+#### Task Organizers
+
+Stelios Andreadis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece, andreadisst@iti.gr
+Ilias Gialampoukidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Anastasios Karakostas, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Stefanos Vrochidis, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Ioannis Kompatsiaris, Information Technologies Institute - Centre of Research and Technology Hellas (ITI - CERTH), Greece
+Roberto Fiorin, Alto Adriatico Water Authority (AAWA), Italy
+Daniele Norbiato, Alto Adriatico Water Authority (AAWA), Italy
+Michele Ferri, Alto Adriatico Water Authority (AAWA), Italy
+
+#### Task Auxiliaries
+
+Benjamin Bischke, German Research Center for Artificial Intelligence (DFKI), Germany
+Patrick Helber, German Research Center for Artificial Intelligence (DFKI), Germany
+
+#### Task Schedule
+* 31 July: Data release
+* ~~23 October~~ 9 November: Runs due
+* 16 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop (Fully virtual)
+
+Workshop will be held online.
+
+
+
+**Acknowledgment:** This task has been supported by the EU’s Horizon 2020 research and innovation programme under grant agreements H2020-700475 [beAWARE](https://beaware-project.eu/), H2020-776019 [EOPEN](https://eopen-project.eu/) and H2020-832876 [aqua3S](https://aqua3s.eu/).
diff --git a/_editions/2020/tasks/gamestory.md b/_editions/2020/tasks/gamestory.md
index db1b048c6..485ec3efc 100644
--- a/_editions/2020/tasks/gamestory.md
+++ b/_editions/2020/tasks/gamestory.md
@@ -2,61 +2,79 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "Emotional Mario: Believable AI agents in video games"
+subtitle:
+blurb: The Emotional Mario task explores the use of human emotion to improve the performance of AI-based agents playing Super Mario Bros. We provide a multimodal dataset consisting of video and sensor data to be used to complete two different subtasks.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+Emotional Mario is structured into two subtasks:
+
+1. In the first subtask, *Artificial Skill*, participants produce an agent that is able to play Super Mario Bros as well as possible. The goal of this task is for the agent (Mario) to get as far as possible in the game (number of levels beat) with the least amount of lives lost.
+2. In the second subtask, *Human or Not*, participants of the Emotional Mario task are expected to create an AI that controls Mario throughout the game. We ask participants to submit AIs for two levels of skill: (i) skilled player and (ii) novice player. A jury will then be presented with videos from human players and AIs in a random sequence and has to judge whether the playthrough in the video originates from a skilled or novice player and if they think a human or an AI controls the game, just like in a Turing test.
-#### Introduction
+Minimizing time and maximizing score and progress has been topic to the Mario AI competition [3]. In the Emotional Mario task, we go beyond these goals and create a more humanlike AI agent that is informed by human emotion. The task provides a data set of videos and sensor readings of people playing Super Mario Bros. Participants use this data set as a source of information about human players' emotional reactions.
+#### Motivation and Background
+With the rise of deep learning, many large leaps in AI research have been achieved in recent years, such as human-level image recognition or text classification. In tasks like image classification, autonomous driving, or personal assistants, researchers have been able to create believable AI agents that behave like human beings. However, in certain areas, the behavior of AI agents remains far from humanlike. This gap is strikiingly evident with AI agents that are created to play repetitive video games. Human players readily recognize the machine nature of such agents. In the Emotional Mario task, we attempt to address this gap by asking researchers to create more believable AI agents that play one of the most iconic classic video games: Super Mario Bros.
-#### New for 2020
-
-
-#### Target group
+Artificial intelligence in commercial video games is only rarely genuinely intelligent. The behavior of bots (seemingly autonomous players) and non-player characters (NPCs) is typically scripted, rule-based, or utilizes finite-state machines. One of the reasons for that is that the goal of game AI is typically not to simulate life, but to create an experience for players that fits the expectations of the game designers [1]. However, with the advent of multiplayer games, several game genres have lost a lot of popularity. Games heavily relying on strategy, like StarCraft, League of Legends, DOTA, or Counter-Strike, are typically played against other human players, as scripted bots, and even highly sophisticated machine learning-based agents provide the quality of experience expected by players [2]. However, in the Mario AI competition, it has been noted that many of the agents entering the competition behaved non-human like [3]. We strongly believe that AI behaving more human-like is a necessary next step. Not only for interactive entertainment and media content creation, where methods for human-like AI could reduce development times drastically, but also serious and educational games, conversational AIs like personal assistants, and behavior of robots and autonomous vehicles would greatly benefit from research in that direction.
+#### Target Group
+The target group for this task is diverse and broad. It includes researchers and practitioners from game design and development, game studies, machine learning, and artificial intelligence and interactive multimedia. We also encourage interdisciplinary research involving people from the media sciences, the cultural sciences, and the humanities discussing what the expected behavior could be and how they are perceived. There are multiple angles from which one can approach this task, from classical decision trees and rule-based AI down to reinforcement learning and even generative adversarial networks. In any case, regardless of the research background, submission will help to have a basic understanding of how human-like behavior is perceived and, ideally, how to mimic it.
#### Data
-
-
-#### Ground Truth
-
+We provide a multimodal dataset, Toadstool [4], consisting of ten participants playing Super Mario Bros. The dataset contains their game input, demographics, sensor output from a medical-grade device, and videos of their faces while playing. The data is free to use for academic purposes.
#### Evaluation Methodology
+The task consists of two subtasks. Both tasks are mandatory for participation. The Artificial Skill subtask is meant to measure the skill of the AI player. The evaluation metric is defined as the number of completed levels and lost lives within 10 minutes of playtime (levels divided by lost lives).
+The second subtask, called Human or Not, will be evaluated by a jury of people with different levels of experience playing computer games (at least two, expert/non-expert). The jury will be presented with a series of videos and they must identify which videos were made with a human playing the game and which videos were made with an AI playing. The possibilities between a human and an AI player is represented with a 10-point scale. The length of the video is 5 minutes with a specific set of levels. The organizers will provide some videos themself from AI and real players.
-#### References and recommended reading
+
+**Submission**
-### Big Picture of the Task
+**Sub Task 1 (artificial skill)**: The actions for an agent playing 10 minutes super Mario in the OpenAI Gym with Settings provided by the organizers.
-#### Innovation
+**Sub Task 2 (human or not)**: 5 minutes video from an agent playing super Mario in the OpenAI Gym with the Settings provided by the organizers.
-#### Focus
-
-
-#### Risk management
-
-
-#### Task organization team
+#### References and recommended reading
+
+
+[1] Rabin, S. (2014). [Game AI pro: collected wisdom of game AI professionals](http://www.gameaipro.com/). AK Peters/CRC Press.
+[2] Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., ... & Józefowicz, R. (2019). [Dota 2 with Large Scale Deep Reinforcement Learning](https://arxiv.org/abs/1912.06680).
-#### Task organizers
+[3] J. Togelius, S. Karakovskiy and R. Baumgarten, [The 2009 Mario AI Competition](https://ieeexplore.ieee.org/document/5586133), IEEE Congress on Evolutionary Computation, Barcelona, 2010, pp. 1-8, doi:10.1109/CEC.2010.5586133.
+[4] Henrik Svoren, Vajira Thambawita, Pål Halvorsen, Petter Jakobsen, Enrique Garcia-Ceja, Farzan Majeed Noori, Hugo L. Hammer, Mathias Lux, Michael Alexander Riegler, and Steven Alexander Hicks. 2020. [Toadstool: A Dataset for Training Emotional Intelligent Machines Playing Super Mario Bros.](https://dl.acm.org/doi/abs/10.1145/3339825.3394939) In Proceedings of the 11th ACM Multimedia Systems Conference (MMSys ’20). Association for Computing Machinery, New York, NY, USA, 309–314.
-#### Task auxiliaries
+#### Task Organizers
+
+* Mathias Lux, Alpen-Adria-Universität Klagenfurt, mlux (at) itec.aau.at
+* Michael Riegler, SimulaMet, michael (at) simula.no
+* Steven Hicks, SimulaMet, steven (at) simula.no
+* Duc-Tien Dang-Nguyen, University of Bergen
+* Kristine Jorgensen, University of Bergen
+* Martin Potthast, Universität Leipzig
+* Vajira Thambawita, SimulaMet
+* Pål Halvorsen, SimulaMet
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~31 October~~ 16 November: Runs due
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
+
diff --git a/_editions/2020/tasks/lifelogging.md b/_editions/2020/tasks/lifelogging.md
index db1b048c6..b13a58ec1 100644
--- a/_editions/2020/tasks/lifelogging.md
+++ b/_editions/2020/tasks/lifelogging.md
@@ -2,61 +2,112 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "Insight for Wellbeing: Multimodal personal health lifelog data analysis"
+subtitle:
+blurb: The quality of the air that we breathe as individuals as we go about our daily lives is important for health and wellbeing, However, measuring personal air quality remains a challenge. This task investigates the prediction of personal air quality using open data or data from lifelogs. The data includes images, tags, physiological data, and sensor readings.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+Task participants create systems that derive insights from multimodal lifelog data that are important for health and wellbeing. The first dataset, namely "personal air quality data" (PAQD), includes air pollution data (PM2.5, O3, and NO2) and lifelog data (e.g., physiological data, tags, and images) collected by using sensors boxes, lifelog cameras, and smartphones along the predefined routes in a city. The second dataset, namely "global air quality data" (GAQD), includes weather and air pollution data collected over the city and provided by the government and crawled from related websites.
-#### Introduction
+Participants in this task tackle two challenging subtasks:
+1. Personal Air Quality Prediction with public/open data: Task participants predict the value of personal air pollution data (PM2.5, O3, and NO2) using only weather data (wind speed, wind direction, temperature, humidity) and air pollution data (PM2.5, O3, and NO2) from public/open data sources (e.g., stations, website). This subtask's target is to investigate whether we can use public/open data to predict personal air pollution data. The personal air pollution data can be concerned as the regional air pollution data since these data a locally collected by people who carry personal equipment. In other words, the ground truth is data collected by sensor boxes carried by people.
+2. Personal Air Quality Prediction with lifelog data: participants predict the personal Air Quality Index using images captured by people (plus GAQD). The purpose of this subtask is whether we can use only lifelog data (i.e., pictures of the surrounding environment, annotations, and comments), plus some data from open sources (e.g., weather, air pollution data) to predict the personal air pollution data.
+#### Motivation and Background
+The association between people's wellbeing and the properties of the surrounding environment is an essential area of investigation. Although these investigations have a long and rich history, they have focused on the general population. There is a surprising lack of research investigating the impact of the environment on the scale of individual people. On a personal scale, local information about air pollution (e.g., PM2.5, NO2, O3), weather (e.g., temperature, humidity), urban nature (e.g., greenness, liveliness, quietness), and personal behavior (e.g., psychophysiological data) play an essential role. It is not always possible to gather plentiful amounts of such data. As a result, a key research question remains open: Can sparse or incomplete data be used to gain insight into wellbeing? Is there a hypothesis about the associations within the data so that wellbeing can be understood using a limited amount of data? Developing hypotheses about the associations within the heterogeneous data contributes towards building good multimodal models that make it possible to understand the impact of the environment on wellbeing at the local and individual scale. Such models are necessary since not all cities are fully covered by standard air pollution and weather stations, and not all people experience the same reaction to the same environment situation. Moreover, images captured by the first-person view could give essential cues to understand that environmental situations in cases in which precise data from air pollution stations are lacking.
-#### New for 2020
+Let us imagine the following scenario. Yamamoto-san is using the Image-2-AQI app to know how harmful air pollution is by merely feeding captured images to the app. Simultaneously, at the urban air pollution center, the air pollution map is updated with Yamamoto-san's contribution (e.g., images, annotation). Satoh-san, with some clicks on his smartphone, the environmental-based risk map application can show him the excellent route from A to B with less congestion and harmful air pollution. Simultaneously, less congestion from A to B is due to fewer people coincidentally traveling on the same route. Such simple apps are parts of the human-environment sustainable and co-existing system that have changed people's pro-environmental behaviors.
+The critical research question here is, "does the personal air quality be predicted by using other data that is easy to obtain?"
-#### Target group
+#### Target Group
+This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science.
#### Data
+The personal air quality data (PAQD) were collected from March to April 2019 along the marathon course of the Tokyo 2020 Olympics and the running course around the Imperial Palace using wearable sensors. There were five data collection participants assigned to five routes to collect the data. Routes 1–4 were along the marathon course for the Tokyo 2020 Olympics. Route 5 was the running course around the Imperial Palace. The length of each route was approximately 5 km. Each participant started data collection at 9 am every weekday, and it took approximately one hour to walk each route. Collected data contain weather data (e.g., temperature, humidity), atmospheric data (e.g., O3, PM2.5, and NO2), GPS data, and lifelog data (e.g., images, annotation).
+The glocal air pollution data (GAPD) contains the atmospheric monitoring station data collected by the Atmospheric Environmental Regional Observation System (AEROS) in Japan (http://soramame.taiki.go.jp). AEROS contains real-time atmospheric data at every hour for 2032 meteorological monitoring stations across Japan. The atmospheric data includes eleven types of air pollutant data (SO2, NOx, NO, NO2, CO, Ox, NMHC, CH4, THC, SPM, and PM2.5), and four types of meteorological data (wind direction, wind speed, temperature, and humidity).
-#### Ground Truth
-
+All data are stored in CSV format, except images in JPG format. Personal data are privacy protected. All task participants should sign the agreement of using these data, released by MediaEval and NICT-Japan, for research purposes only.
#### Evaluation Methodology
+The ground truth for the dataset of the two subtasks is collected as follows:
+- For the Personal Air Quality Prediction with public/open data subtask: some parts of personal (PM2.5, O3, and NO2) data are deleted and saved as the ground truth.
+- For the Personal Air Quality Prediction with lifelog data subtask: the set of personal AQI are hidden and saved as the ground truth
+
+For each subtask, the evaluation method is applied as follows:
+- For the Personal Air Quality Prediction with public/open data subtask: We use the SMAPE/RMSE/MAE for comparing each air pollution factor PM2.5, O3, and NO2 with the ground truth.
+- For the Personal Air Quality Prediction with lifelog data subtask: We use the SMAPE/RMSE/MAE for comparing predicted AQI to the ground truth.
+
+The formulation for computing AQI value from (PM2.5, O3, and NO2) data can be found at
+https://en.wikipedia.org/wiki/Air_quality_index (prefer the "Computing the AQI" section)
+
+https://airtw.epa.gov.tw/ENG/Information/Standard/AirQualityIndicator.aspx (prefer the "real-time table" that is the look-up table for C_low, C_high, I_low, I_high value)
#### References and recommended reading
+
+
+[1] Sato, T., Dao, M.S., Kuribayashi, K., and Zettsu, K.: [SEPHLA: Challenges and Opportunities within Environment – Personal Health Archives](https://link.springer.com/chapter/10.1007/978-3-030-05710-7_27), MMM 2018.
+
+[2] P. Zhao and K. Zettsu, [Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution](https://ieeexplore.ieee.org/document/9006604), 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 5620-5629.
+
+[3] N. Nguyen, M. Dao and K. Zettsu, [Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data](https://ieeexplore.ieee.org/abstract/document/9005985), 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 1669-1674.
+[4] P. Vo, T. Phan, M. Dao and K. Zettsu, [Association Model between Visual Feature and AQI Rank Using Lifelog Data](https://ieeexplore.ieee.org/document/90056360), 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 4197-4200.
-### Big Picture of the Task
+[5] Dao, M. S., Zhao, P., Sato, T., Zettsu, K., Dang-Nguyen D. T., Gurrin, C., Nguyen, N. T.: Overview of MediaEval 2019: Insights for Wellbeing TaskMultimodal Personal Health Lifelog Data Analysis, MediaEval Benchmarking Initiative for Multimedia Evaluation (MediaEval 2019), Antipolis, France (November 2019).
-#### Innovation
+[6] Song, H., Lane, K. J., Kim, H., Kim, H., Byun, G., Le, M., Choi, Y., Park, C. R., & Lee, J. T. (2019). [Association between Urban Greenness and Depressive Symptoms: Evaluation of Greenness Using Various Indicators](https://pubmed.ncbi.nlm.nih.gov/30634488/), International journal of environmental research and public health, 16(2), 173.
+[7] Darshan Santani, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2018. [Looking South: Learning Urban Perception in Developing Cities](https://dl.acm.org/doi/10.1145/3224182), Trans. Soc. Comput. 1, 3, Article 13 (December 2018), 23 pages.
-#### Focus
+[8] Anh-Vu Mai-Nguyen, Trong-Dat Phan, Anh-Khoa Vo, Van-Luon Tran, Minh-Son Dao, and Koji Zettsu. 2020. [BIDAL-HCMUS@LSC2020: An Interactive Multimodal Lifelog Retrieval with Query-to-Sample Attention-based Search Engine](https://dl.acm.org/doi/10.1145/3379172.3391722), In Proceedings of the Third Annual Workshop on Lifelog Search Challenge (LSC ’20). Association for Computing Machinery, New York, NY, USA, 43–49.
+[9] Tan-Loc Nguyen-Tai, Dang-Hieu Nguyen, Minh-Tam Nguyen, Thanh-Duong Nguyen, Thanh-Hai Dang, and Minh-Son Dao. 2020. [MNR-HCM Data: A Personal Lifelog and Surrounding Environment Dataset in Ho-Chi-Minh City, Viet Nam](https://dl.acm.org/doi/10.1145/3379174.3392320), In Proceedings of the 2020 Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR ’20). Association for Computing Machinery, New York, NY, USA, 21–26.
-#### Risk management
+[10] Vahdatpour, M., Sajedi, H. & Ramezani, F. [Air pollution forecasting from sky images with shallow and deep classifiers](https://link.springer.com/article/10.1007/s12145-018-0334-x), Earth Sci Inform 11, 413–422 (2018).
+#### Task Organizers,
+Minh-Son Dao (NICT, Japan) dao (at) nict.go.jp
-#### Task organization team
+Peijiang Zhao (NICT, Japan) dlzpj (at) nict.go.jp
+Ngoc-Thanh Nguyen (UIT, Vietnam) thanhnn.13 (at) grad.uit.edu.vn
-#### Task organizers
+Thanh-Binh Nguyen (HCMUS, Vietnam) ngtbinh (at) hcmus.edu.vn
+Duc-Tien Dang-Nguyen (UiB, Norway) ductien.dangnguyen (at) uib.no
-#### Task auxiliaries
+Cathal Gurrin (DCU, Ireland) cgurrin (at) computing.dcu.ie
+#### Task Auxiliaries
+
+Tan-Loc Nguyen-Tai (UIT, Vietnam)
+
+Dang-Hieu Nguyen (UIT, Vietnam)
+
+Minh-Tam Nguyen (UIT, Vietnam)
+
+Quoc-Dat Duong (HCMUS, Vietnam)
+
+Minh-Quan Le (HCMUS, Vietnam)
+
+Trong-Dat Phan (HCMUS, Vietnam)
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~30 October~~ 16 November: Runs due + Start writing Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop (Fully virutal)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/medico.md b/_editions/2020/tasks/medico.md
index 48077f82a..8237db040 100644
--- a/_editions/2020/tasks/medico.md
+++ b/_editions/2020/tasks/medico.md
@@ -5,58 +5,85 @@ year: 2020
hide: false
# required info
-title: Medico 2020
-subtitle:
-blurb: The fight against colorectal cancer requires better diagnosis tools. Computer-aided diagnosis systems can reduce the chance that diagnosticians overlook a polyp during a colonoscopy. This task focuses on robust and efficient algorithms for polyp segmentation. The data consists of a large number of endoscopic images of the colon.
+title: Medico
+subtitle: Semantic polyp segmentation
+blurb: "The fight against colorectal cancer requires better diagnosis tools. Computer-aided diagnosis systems can reduce the chance that diagnosticians overlook a polyp during a colonoscopy. This task focuses on robust and efficient algorithms for polyp segmentation. The data consists of a large number of endoscopic images of the colon."
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Challenge Description
+The "Medico automatic polyp segmentation challenge" aims to develop computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller or flat polyps) with high efficiency and accuracy. The main goal of the challenge is to benchmark semantic segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization.
-#### Introduction
+Participants will get access to a dataset consisting of 1,000 segmented polyp images from the gastrointestinal tract and a separate testing dataset. The challenge consists of two mandatory tasks, each focused on a different requirement for efficient polyp detection. We hope that this task encourages multimedia researchers to apply their vast knowledge to the medical field and make an impact that may affect real lives.
+#### Task Description
+The participants are invited to submit the results on the following tasks:
-#### New for 2020
+1) Polyp segmentation task (required) - The polyp segmentation task asks participants to develop algorithms for segmenting polyps on a comprehensive dataset.
+2) Algorithm efficiency task (required) - The algorithm efficiency task is similar to task one but puts a stronger emphasis on the algorithm's speed in terms of frames-per-second. To ensure a fair evaluation, **this task requires participants to submit a Docker image** so that all algorithms are evaluated on the same hardware.
-#### Target group
+#### Motivation and Background
+Colonoscopy is currently the gold-standard medical procedure for examining the colon for lesions and other abnormalities such as cancer. Colorectal cancer (CRC) is the third most prevailing strain in terms of cancer incidence and second in terms of mortality globally. As early detection is critical for patient survival, regular screening through colonoscopy is a prerequisite for cancer detection and colorectal cancer prevention. Regardless of the success of colonoscopy, it is still estimated to miss around 20% of polyps. This is mostly due to the heavy reliance on the skill of the clinician operating the endoscope and his/her ability to detect polyps. An automated computer-aided diagnosis (CAD) system could be one of the potential solutions for the overlooked polyps. Such detection or surveillance systems can give doctors a so-called "third-eye", thereby alerting them of missed polyps or other common lesions.
+#### Target Group
+The task is of interest to the researchers working with multimedia segmentation, deep learning (semantic segmentation), and computer vision. We especially encourage young researchers to contribute to the field of endoscopy by developing an automated computer-aided diagnosis system that could be potentially used in clinical settings.
#### Data
+The dataset contains 1,000 polyp images and their corresponding ground truth mask. The datasets were collected from real routine clinical examinations at Vestre Viken Health Trust (VV) in Norway by expert gastroenterologists. The VV is the collaboration of the four hospitals that provide healthcare service to 470,000 peoples. The resolution of images varies from 332✕487 to 1920✕1072 pixels. Some of the images contain green thumbnail in the lower-left corner of the images showing the position marking from the ScopeGuide (Olympus). The training dataset can be downloaded from [https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/).
-
-#### Ground Truth
-
+The test dataset is now released. It can be downloaded from [https://drive.google.com/file/d/1uP2W2g0iCCS3T6Cf7TPmNdSX4gayOrv2/view?usp=sharing](https://drive.google.com/file/d/1uP2W2g0iCCS3T6Cf7TPmNdSX4gayOrv2/view?usp=sharing).
#### Evaluation Methodology
+The task will use mean Intersection over Union (mIoU) or Jaccard index as an evaluation metric, which is a standard metric for all medical segmentation task. If the teams have the same mIoU values, then the teams will be further evaluated on the basis of the higher value of the dice coefficient. We strongly recommend calculating other standard evaluation metrics such as dice coefficient, precision, recall, F2, and frame per second (FPS) for a comprehensive evaluation.
+In the challenge overview paper, the organizers will be calculating the metrics such as the Dice coefficient, mIoU, Recall, Precision,Overlap, F2, MAE, FPS, s-score, and Clinical relevance of the methods submitted by each team.
-#### References and recommended reading
+#### Submission
+The submissions will be verified by inspecting the corresponding Docker image. If you have a problem with submitting the Docker image, we are flexible to accept a zip file that contains the predicted mask for task 1. For task 2, we require the Docker submission so that we can evaluate on the same hardware. For the more instruction about the Docker submission, please refer to our [GitHub](https://github.com/DebeshJha/Medico-automatic-polyp-segmentation-challenge/tree/master/submission) repository.
-### Big Picture of the Task
+#### Rules
+By registering this challenge, each individual or team agrees to use only the provided dataset. After the challenge, the dataset test dataset will be made available and the authors can only use it for publication or any non-commercial use. A participating team will be allowed to make a maximum of 5 submissions. The test data can not be used while training the model. The results will be evaluated by the organizers and presented at MediaEval 2020.
-#### Innovation
+#### Discord Channel
+To facilitate communication within the participants, we have set up a Discord channel. You can use this channel for discussion of the challenge or ask questions. Please email debesh@simula.no for the invitation at the Discord.
-#### Focus
-
+#### References and recommended reading
+
+
-#### Risk management
+1. Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D. Johansen. 2020. [Kvasir-seg: A segmented polyp dataset](https://link.springer.com/chapter/10.1007/978-3-030-37734-2_37), In Proceeding of International Conference on Multimedia Modeling (MMM), 451-462.
+2. Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D. Johansen. 2019. [Resunet++: An advanced architecture for medical image segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8959021), In International Symposium on Multimedia (ISM), 225-2255.
-#### Task organization team
+3. Tobias Ross et al. 2020. [Robust Medical Instrument Segmentation Challenge 2019](https://arxiv.org/abs/2003.10299), arXiv preprint.
+4. Sharib Ali et al. 2020. [An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy](https://www.nature.com/articles/s41598-020-59413-5.pdf), Scientific Reports.
-#### Task organizers
+#### Task Organizers
+
-#### Task auxiliaries
+* Debesh Jha, SimulaMet debesh (at) simula.no,
+* Steven Hicks, SimulaMet steven (at) simula.no, SimulaMet
+* Michael Riegler, SimulaMet
+* Pål Halvorsen, SimulaMet and OsloMet
+* Konstantin Pogorelov, Simula Research Laboratory
+* Thomas de Lange, Sahlgrenska University Hospital, Mölndal, Sweden, and Bærum Hospital, Vestre Viken, Norway.
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 01 July: Data release
+* 01 September: Test Data release
+* ~~31 October~~ 16 November: Runs due
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
diff --git a/_editions/2020/tasks/memorability.md b/_editions/2020/tasks/memorability.md
index 9273a3d3c..196dbc778 100644
--- a/_editions/2020/tasks/memorability.md
+++ b/_editions/2020/tasks/memorability.md
@@ -5,71 +5,85 @@ year: 2020
hide: false
# required info
-title: The 2020 Predicting Media Memorability Task
-subtitle: Predicting Media Memorability
-blurb: Media platforms such as social networks, media advertisement, information retrieval and recommendation systems deal with exponentially growing data day after day. Enhancing the relevance of multimedia occurrences in our everyday life requires new ways to organize – in particular, to retrieve – digital content. Like other metrics of video importance, such as aesthetics or interestingness, memorability can be regarded as useful to help make a choice between competing videos. This is even truer when one considers the specific use cases of creating commercials or creating educational content. Because the impact of different multimedia content, images or videos, on human memory is unequal, the capability of predicting the memorability level of a given piece of content is obviously of high importance for professionals in the field of advertising. Beyond advertising, other applications, such as filmmaking, education, content retrieval, etc., may also be impacted by the proposed task.
+title: Predicting Media Memorability
+subtitle:
+blurb: "The task requires participants to automatically predict memorability scores for videos, that reflect the probability for a video to be remembered. Participants will be provided with an extensive data set of videos with memorability annotations, related information, and pre-extracted state-of-the-art visual features."
---
-### Task Description
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-#### Introduction
+#### News
+* 19 October: Latest task schedule published
+* 12 October: Testing set videos and features have been released
+* 30 September: More annotations for the training set have been released
+#### Help for Annotations
+We need more annotations for the dataset. We kindly ask for your help to get more annotations. Please visit the [link](https://annotator.uk/mediaeval/index.php) and participate in the funny game to contribute to the dataset. Thanks in advance for your contribution.
+
+#### Task Description
Media platforms such as social networks, media advertisement, information retrieval and recommendation systems deal with exponentially growing data day after day. Enhancing the relevance of multimedia occurrences in our everyday life requires new ways to organize – in particular, to retrieve – digital content. Like other metrics of video importance, such as aesthetics or interestingness, memorability can be regarded as useful to help make a choice between competing videos. This is even truer when one considers the specific use cases of creating commercials or creating educational content. Because the impact of different multimedia content, images or videos, on human memory is unequal, the capability of predicting the memorability level of a given piece of content is obviously of high importance for professionals in the field of advertising. Beyond advertising, other applications, such as filmmaking, education, content retrieval, etc., may also be impacted by the proposed task.
The task requires participants to automatically predict memorability scores for videos, that reflect the probability for a video to be remembered. Participants will be provided with an extensive data set of videos with memorability annotations, related information, and pre-extracted state-of-the-art visual features.
-#### New for 2020
+#### Background and Motivation
+Understanding what makes a video memorable has a very broad range of current applications, e.g., education and learning, content retrieval and search, content summarization, storytelling, targeted advertising, content recommendation and filtering. Efficient memorability prediction models will also push forward the semantic understanding of multimedia content, by putting human cognition and perception in the center of the scene understanding.
+
In this 3rd edition of the task, a more robust collection of videos is provided, which is retrieved from the TREC Video Retrieval Evaluation (TRECVID) task. Optionally, we may use descriptive captions from their use in the TRECVid automatic video captioning task.
-#### Target group
+#### Target Group
Researchers will find this task interesting if they work in the areas of human perception and scene understanding, such as image and video interestingness, memorability, attractiveness, aesthetics prediction, event detection, multimedia affect and perceptual analysis, multimedia content analysis, machine learning (though not limited to).
#### Data
-Data is composed of 6,000 short videos retrieved from TRECVid. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task [6], memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorization of the videos (short term), and then 24 to 72 hours later (long term). The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Aesthetic Features, C3D, Captions, Colour Histograms, HMP, HoG, Fc7 layer from InceptionV3, LBP, or ORP. In comparison to the videos used in this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view.
+Data is composed of 6,000 short videos retrieved from TRECVid 2019 Video to Text dataset [1]. Each video consists of a coherent unit in terms of meaning and is associated with two scores of memorability that refer to its probability to be remembered after two different durations of memory retention. Similar to previous editions of the task [2], memorability has been measured using recognition tests, i.e., through an objective measure, a few minutes after the memorization of the videos (short term), and then 24 to 72 hours later (long term).
-#### Ground Truth
-The ground truth for memorability will be collected through recognition tests, and thus results from objective measures of memory performance.
+Now, a subset of dataset is available including 590 videos as part of the training set. The ground truth of the development data will be enhanced with more annotators per movie with the release of the test data. This would allow to experiment whether increasing the annotations' agreement has a direct influence on the prediction quality. Nevertheless, methods should cope with a lower annotator agreement, which is specific to such subjective tasks.
+
+The videos are shared under Creative Commons licenses that allow their redistribution. They come with a set of pre-extracted features, such as: Aesthetic Features, C3D, Captions, Colour Histograms, HMP, HoG, Fc7 layer from InceptionV3, LBP, or ORP. In comparison to the videos used in this task in 2018 and 2019, the TRECVid videos have much more action happening in them and thus are more interesting for subjects to view.
#### Evaluation Methodology
+The ground truth for memorability will be collected through recognition tests, and thus results from objective measures of memory performance.
+
The outputs of the prediction models – i.e., the predicted memorability scores for the videos – will be compared with ground truth memorability scores using classic evaluation metrics (e.g., Spearman’s rank correlation).
#### References and recommended reading
-[1] Aditya Khosla, Akhil S Raju, Antonio Torralba, and Aude Oliva. 2015. Understanding and predicting image memorability at a large scale. In Proc. IEEE Int. Conf. on Computer Vision (ICCV). 2390–2398.\\
-[2] Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva. 2014. What makes a photograph memorable? IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 7 (2014), 1469–1482.\\
-[3] Hammad Squalli-Houssaini, Ngoc Duong, Gwenaëlle Marquant, and Claire-Hélène Demarty. 2018. Deep learning for predicting image memorability. In Proc. IEEE Int. Conf. on Audio, Speech and Language Processing (ICASSP).\\
-[4] Junwei Han, Changyuan Chen, Ling Shao, Xintao Hu, Jungong Han, and Tianming Liu. 2015. Learning computational models of video memorability from fMRI brain imaging. IEEE transactions on cybernetics 45, 8 (2015), 1692–1703.\\
-[5] Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, and Akhil Shetty. 2017. Show and Recall: Learning What Makes Videos Memorable. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2730–2739.\\
-[6] Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. "VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability." Proceedings of the IEEE International Conference on Computer Vision. 2019.\\
-[7] M.G. Constantin, M. Redi, G. Zen, B. Ionescu, "Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates", ACM Computing Surveys, 52(2), 2019.
+
+
+[1] Awad, G., Butt, A.A., Lee, Y., Fiscus, J., Godil, A., Delgado, A., Smeaton, A.F. and Graham, Y., [Trecvid 2019: An evaluation campaign to benchmark video activity detection, video captioning and matching, and video search & retrieval](https://www-nlpir.nist.gov/projects/tvpubs/tv19.papers/tv19overview.pdf). 2019.
-### Big Picture of the Task
+[2] Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://openaccess.thecvf.com/content_ICCV_2019/papers/Cohendet_VideoMem_Constructing_Analyzing_Predicting_Short-Term_and_Long-Term_Video_Memorability_ICCV_2019_paper.pdf). Proceedings of the IEEE International Conference on Computer Vision. 2019.
-#### Innovation
-The computational understanding of video memorability (VM) follows on from the study of image memorability prediction which has attracted increasing attention since the seminal work of Isola et al. [2]. Models achieved very good results at predicting image memorability [1, 3]. In contrast, research on VM from a computer science point of view is in its early stage. The scarcity of studies on VM can be explained by several reasons. Firstly, there is no publicly available data set to train and test models. The second point, closely related to the previous one, is the lack of a common definition for VM. Regarding modelling, the previous attempts at predicting VM [5, 6] highlighted several features which contribute to the prediction of VM, such as semantic, saliency and color features, but the work is far from complete and our capacity to propose efficient computational models will help to meet the challenge of VM prediction. The goal of this task is to participate in the harmonization and the advancement of this emerging search field. Furthermore, in contrast to previous work on image memorability prediction, where memorability was measured a few minutes after memorization, we propose a dataset with “long-term” memorability annotations. We expect the predictions of the models trained on this data to be more representative of long-term memory, which is used preferably in numerous applications.
+[3] Aditya Khosla, Akhil S Raju, Antonio Torralba, and Aude Oliva. 2015. [Understanding and predicting image memorability at a large scale](https://people.csail.mit.edu/khosla/papers/iccv2015_khosla.pdf). In Proc. IEEE Int. Conf. on Computer Vision (ICCV). 2390–2398.
-#### Focus
-Understanding what makes a video memorable has a very broad range of current applications, e.g., education and learning, content retrieval and search, content summarization, storytelling, targeted advertising, content recommendation and filtering. Efficient memorability prediction models will also push forward the semantic understanding of multimedia content, by putting human cognition and perception in the center of the scene understanding.
+[4] Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva. 2014. [What makes a photograph memorable?](http://web.mit.edu/phillipi/www/publications/memory_pami.pdf) IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 7 (2014), 1469–1482.
-#### Risk management
-The task has been successfully organised in 2018 and 2019. The experience gained will help us to anticipate and overcome the inherent difficulties in organizing such a task.
+[5] Hammad Squalli-Houssaini, Ngoc Duong, Gwenaëlle Marquant, and Claire-Hélène Demarty. 2018. [Deep learning for predicting image memorability](https://hal.archives-ouvertes.fr/hal-01629297/file/main.pdf). In Proc. IEEE Int. Conf. on Audio, Speech and Language Processing (ICASSP).
-#### Task organization team
+[6] Junwei Han, Changyuan Chen, Ling Shao, Xintao Hu, Jungong Han, and Tianming Liu. 2015. [Learning computational models of video memorability from fMRI brain imaging](https://ieeexplore.ieee.org/abstract/document/6919270). IEEE transactions on cybernetics 45, 8 (2015), 1692–1703.
-The task benefits from a team from three different research sites and countries, and from different research fields, that have complementary expertise. Most of the organizers already have experience in organizing tasks in the context of the MediaEval and ImageCLEF, and are experts in their fields. University of Essex and University Politehnica of Bucharest are in charge of the creation of the video-based subtask collection. Otherwise, the different teams – University of Essex, University Politehnica of Bucharest and InterDigital – will be involved and will interact in all the aspects of the task (management, dissemination, etc.).
+[7] Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, and Akhil Shetty. 2017. [Show and Recall: Learning What Makes Videos Memorable](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w40/Shekhar_Show_and_Recall_ICCV_2017_paper.pdf). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2730–2739.
-#### Task organizers
+[8] M.G. Constantin, M. Redi, G. Zen, B. Ionescu, [Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates](http://campus.pub.ro/lab7/bionescu/index_files/pub/2018_ACM_CSUR-draft.pdf), ACM Computing Surveys, 52(2), 2019.
-Alba García Seco de Herrera, Rukiye Savran Kiziltepe, Faiyaz Doctor University of Essex, UK;\\
-Mihai Gabriel Constantin, Bogdan Ionescu, University Politehnica of Bucharest, Romania;\\
-Alan Smeaton, Graham Healy, Dublin City University, Ireland;\\
-Claire-Hélène Demarty, InterDigital, R&I, France.
+#### Task organizers
+* Alba García Seco de Herrera, alba.garcia (at) essex.ac.uk, University of Essex, UK
+* Rukiye Savran Kiziltepe, rs16419 (at) essex.ac.uk, University of Essex, UK
+* Faiyaz Doctor, fdocto (at) essex.ac.uk, University of Essex, UK
+* Mihai Gabriel Constantin, cmihaigabriel (at) gmail.com, University Politehnica of Bucharest, Romania
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+* Alan Smeaton, Dublin City University, Ireland
+* Claire-Hélène Demarty, InterDigital, R&I, France
#### Task auxiliaries
-Jon Chamberlain, University of Essex, UK.
+* Jon Chamberlain, University of Essex, UK
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* ~~21~~ 31 August: Data release
+* ~~15~~ ~~31 October~~ 16 November: Runs due
+* ~~15~~ 23 November: Results returned
+* 30 November: Working notes paper
+* Early December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
+
diff --git a/_editions/2020/tasks/music.md b/_editions/2020/tasks/music.md
old mode 100644
new mode 100755
index db1b048c6..eaf84d29d
--- a/_editions/2020/tasks/music.md
+++ b/_editions/2020/tasks/music.md
@@ -2,61 +2,83 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: Emotions and Themes in Music
+subtitle: Emotion and Theme Recognition in Music using Jamendo
+blurb: We invite the participants to try their skills in building a classifier to predict the emotions and themes conveyed in a music recording, using our dataset of music audio, pre-computed audio features, and tag annotations (e.g., happy, sad, melancholic). All data we provide comes from Jamendo, an online platform for music under Creative Commons licenses.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
-#### Introduction
+Emotion and theme recognition is a popular task in music information retrieval that is relevant for music search and recommendation systems. We invite the participants to try their skills at recognizing moods and themes conveyed by the audio tracks.
+This task involves the prediction of moods and themes conveyed by a music track, given the raw audio. The examples of moods and themes are: happy, dark, epic, melodic, love, film, space etc. Each track is tagged with at least one tag that serves as a ground-truth.
-#### New for 2020
+Participants are expected to train a model that takes raw audio as an input and outputs the predicted tags. To solve the task, participants can use any audio input representation they desire, be it traditional handcrafted audio features or spectrograms or raw audio inputs for deep learning approaches. We also provide a handcrafted feature set extracted by the [Essentia](https://essentia.upf.edu/documentation/) audio analysis library as a reference. We allow usage of third-party datsets for model development and training, but it needs to be mentioned explicitly.
+
-#### Target group
+#### Target Group
+Researchers in music information retrieval, music psychology, machine learning, and music and technology enthusiasts in general.
#### Data
+The dataset used for this task is the `autotagging-moodtheme` subset of the [MTG-Jamendo dataset](https://github.com/MTG/jamendo-dataset) [1], built using audio data from [Jamendo](https://jamendo.com) and made available under Creative Commons licenses. This subset includes 18,486 audio tracks with mood and theme annotations. In total, there are 57 tags, and tracks can possibly have more than one tag.
-#### Ground Truth
+We also provide pre-computed statistical features from [Essentia](https://essentia.upf.edu) using the feature extractor for [AcousticBrainz](https://acousticbrainz.org/). These features are were previously used in the MediaEval genre recognition tasks in [2017](https://multimediaeval.github.io/2017-AcousticBrainz-Genre-Task/) and [2018](https://multimediaeval.github.io/2018-AcousticBrainz-Genre-Task/).
#### Evaluation Methodology
+Participants should generate predictions for the [test split](https://github.com/MTG/jamendo-dataset/blob/master/data/splits/split-0/autotagging_moodtheme-test.tsv) and submit those to the task organizers.
-#### References and recommended reading
+The generated outputs for the test dataset will be evaluated according to the following metrics that are commonly used in the evaluation of auto-tagging systems: Macro **ROC-AUC** and **PR-AUC** on tag prediction scores. Leaderboard will be based on PR-AUC.
+For reference, [here](https://multimediaeval.github.io/2019-Emotion-and-Theme-Recognition-in-Music-Task/) is the website of the 2019 edition of the task.
-### Big Picture of the Task
-#### Innovation
+#### References and recommended reading
+
+
+[1] Dmitry Bogdanov, Minz Won, Philip Tovstogan, Alastair Porter and Xavier Serra. 2019. [The MTG-Jamendo dataset for automatic music tagging](http://mtg.upf.edu/node/3957). Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019).
-#### Focus
+[2] Dmitry Bogdanov, Alastair Porter, Philip Tovstogan and Minz Won. 2019. [MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo](https://github.com/multimediaeval/2019-Emotion-and-Theme-Recognition-in-Music-Task/blob/master/jamendo-emotion-theme-task-me19.pdf). MediaEval 2019 Workshop.
+[3] Mohammad Soleymani, Micheal N. Caro, Erik M. Schmidt, Cheng-Ya Sha and Yi-Hsuan Yang. 2013. [1000 songs for emotional analysis of music](https://ibug.doc.ic.ac.uk/media/uploads/documents/cmm13-soleymani.pdf). In Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia (CrowdMM 2013), 1-6.
-#### Risk management
+[4] Anna Aljanaki, Yi-Hsuan Yang and Mohammad Soleymani. 2014. [Emotion in music task at MediaEval 2014](http://ceur-ws.org/Vol-1263/mediaeval2014_submission_33.pdf).
+[5] Renato Panda, Ricardo Malheiro and Rui Pedro Paiva. 2018. [Musical texture and expressivity features for music emotion recognition](http://mir.dei.uc.pt/pdf/Conferences/MOODetector/ISMIR_2018_Panda.pdf). In Proceedings of the International Society on Music Information Retrieval Conference (ISMIR 2018), 383-391.
-#### Task organization team
+[6] Cyril Laurier, Owen Meyers, Joan Serra, Martin Blech and Perfecto Herrera. 2009. [Music mood annotator design and integration](http://mtg.upf.edu/files/publications/Laurier_MusicMoodAnnotator.pdf). In 7th International Workshop on Content-Based Multimedia Indexing (CBMI'09), 156-161.
+[7] Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Brandon G. Morton, Patrick Richardson, Jeffrey Scott, Jacquelin A. Speck and Douglas Turnbull. 2010. [Music emotion recognition: A state of the art review](http://ismir2010.ismir.net/proceedings/ismir2010-45.pdf). In Proceedings of the International Society on Music Information Retrieval Conference (ISMIR2010), 255-266.
-#### Task organizers
+[8] Xiao Hu and J. Stephen Downie. 2007. [Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata](http://ismir2007.ismir.net/proceedings/ISMIR2007_p067_hu.pdf). In Proceedings of the International Conference on Music Information Retrieval (ISMIR2007), 67-72.
-#### Task auxiliaries
+#### Task Organizers
+Philip Tovstogan, Music Technology Group, Universitat Pompeu Fabra, Spain
+Dmitry Bogdanov, Music Technology Group, Universitat Pompeu Fabra, Spain
+Alastair Porter, Music Technology Group, Universitat Pompeu Fabra, Spain
+Minz Won, Music Technology Group, Universitat Pompeu Fabra, Spain
+(first.last@upf.edu)
+
+
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 13 July: Data release
+* 6 November: Runs due
+* 15 November: Results returned
+* 30 November: Working notes paper
+* Early December: MediaEval 2020 Workshop
+
+Workshop will be held online. Exact dates to be announced.
diff --git a/_editions/2020/tasks/newsimages.md b/_editions/2020/tasks/newsimages.md
index db1b048c6..900d6fe49 100644
--- a/_editions/2020/tasks/newsimages.md
+++ b/_editions/2020/tasks/newsimages.md
@@ -2,61 +2,85 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "NewsImages: The role of images in online news"
+subtitle:
+blurb: Images play an important role in online news articles and news consumption patterns. This task aims to achieve additional insight about this role. Participants are supplied with a large set of articles (including text body, and headlines) and the accompanying images. The task requires participants to predict which image was used to accompany each article and also predict frequently clicked articles on the basis of accompanying images.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+News articles use both text and images to communicate their message. The overall goal of this task is to better understand the relationship the textual content of the articles (including text body, and headlines) and to understand the impact of these elements on readers’ interest in the news (measured by the number of views)
-#### Introduction
+Participants receive a data set of news articles and accompanying images. The news articles consist of text snippets (first 256 characters) and headlines. They can participate in either (or both) of two subtasks.
+##### Task 1: Image-Text Re-Matching
-#### New for 2020
+News articles contain images that accompany the text. The connection between the images and text is more complex than often realized. In this task, participants predict (from a set of images) which image was published with a given news article. We also ask participants to report their insights into characteristics that connect the text of news articles and the images.
+##### Task 2: News Click Prediction
-#### Target group
+News websites present users with recommendations of what to read next. These are often displayed as the article title plus an accompanying image. In this task, participants investigate whether recommendations that are frequently clicked by users can be predicted using the text content of the article and/or the accompanying image.
+Participants are encouraged to make their code public with their submission.
-#### Data
+#### Motivation and Background
+Online news articles are multimodal: the textual content of an article is often accompanied by an image. The image is important for illustrating the content of the text, but also attracting readers' attention. Research in multimedia and recommender systems generally assumes a simple relationship between images and text occurring together. For example, in image captioning [6], the caption is often assumed to describe the literally depicted content of the image. In contrast, when images accompany news articles, the relationship becomes more complex [8]. The goal of this task is to investigate this complexity in more depth, in order to understand the implications that it may have for the areas of journalism and recommender systems.
+The task is formulated into two straightforward subtasks that participants can address using text-based and/or image features. However, the ultimate objective of this task is to gain additional insight. Specifically, we are curious about the connection between the textual content of articles and the images that accompany them and also about the connection between the image and title shown by a recommender system to users and the tendency of users to click on the recommended article. We are especially interested in aspects of images that go beyond the conventional set of concepts studied by concept detection. We are also interested in aspects of images that go beyond the literally depicted content. Such aspects include color, style and framing.
-#### Ground Truth
+#### Target Group
+This task targets researchers who are interested in the connection between images and text and images and user consumption behavior. This includes people working in the areas of computer vision, recommender systems, cross-modal information retrieval, as well as in the area of news analysis.
+#### Data
+The data set is a large collection of news articles (ca. 10k-15k) from a German publisher that publishes news article recommendations on its website. Each article consists of a headline and a text snippet (first 256 characters) plus the link to download the accompanying image. The data is split into a training set (ground truth provided) and a test set. Participants must crawl their own images. To strictly ensure fair comparison, the final test set will include the test set articles for which all participants could successfully access the images.
+In order to simplify the image analysis, we provide image annotations computed using standard image annotation models (e.g. VGG19) trained on image net. Details about the provided image annoations can be found in the description of the [Multi-Media Recommendation task 2019](http://www.dai-labor.de/fileadmin/Files/Publikationen/Buchdatei/MultiMediaRec2019.pdf) (Section 3.2 - Data).
#### Evaluation Methodology
+##### Task 1: Image-Text Re-Matching
+For each news article in the test set, participants return the top five images that they predict to have accompanied that article. Success is measured with Precision@5.
+##### Task 2: News Click Prediction
+Given a set of news articles, participants predict the topmost news articles that are likely to be clicked when they are recommended. The number of topmost articles will be specified. Success is measured with the number of correct predictions.
+##### Analysis and Insight
+For both tasks, the ultimate goal is to understand news and news consumption behavior. We will also judge participants in terms of the quality of the insight that they achieve about the relationship between text and images and in the relationship between images and news consumption behavior.
#### References and recommended reading
+
+
+[1] Corsini, Francesco, and Martha A. Larson. [CLEF NewsREEL 2016: image based recommendation.](https://repository.ubn.ru.nl/bitstream/handle/2066/161886/161886.pdf) (2016).
+[2] Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007, May). [Google news personalization: scalable online collaborative filtering](https://dl.acm.org/doi/abs/10.1145/1242572.1242610). In Proceedings of the 16th international conference on World Wide Web (pp. 271-280).
-### Big Picture of the Task
-
-#### Innovation
-
+[3] Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., & Huber, A. (2014, October). [Offline and online evaluation of news recommender systems at swissinfo.ch](https://dl.acm.org/doi/abs/10.1145/2645710.2645745). In Proceedings of the 8th ACM Conference on Recommender systems (pp. 169-176).
-#### Focus
+[4] Ge, M., & Persia, F. (2017). [A survey of multimedia recommender systems: Challenges and opportunities.](https://www.worldscientific.com/doi/abs/10.1142/S1793351X17500039) International Journal of Semantic Computing, 11(03), 411-428.
+[5] Hopfgartner, F., Balog, K., Lommatzsch, A., Kelly, L., Kille, B., Schuth, A., & Larson, M. (2019). [Continuous evaluation of large-scale information access systems: a case for living labs.](https://link.springer.com/chapter/10.1007/978-3-030-22948-1_21) In Information Retrieval Evaluation in a Changing World (pp. 511-543). Springer, Cham.
-#### Risk management
+[6] Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019). [A comprehensive survey of deep learning for image captioning.](https://dl.acm.org/doi/abs/10.1145/3295748) ACM Computing Surveys (CSUR), 51(6), 1-36.
+[7] Lommatzsch, A., Kille, B., Hopfgartner, F., Larson, M., Brodt, T., Seiler, J., & Özgöbek, Ö. (2017, September). [CLEF 2017 NewsREEL overview: A stream-based recommender task for evaluation and education.](https://link.springer.com/book/10.1007/978-3-319-65813-1) In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 239-254). Springer, Cham.
-#### Task organization team
+[8] Oostdijk, N., van Halteren, H., Bașar, E., & Larson, M. (2020, May). [The Connection between the Text and Images of News Articles: New Insights for Multimedia Analysis.](https://www.aclweb.org/anthology/2020.lrec-1.535/) In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 4343-4351).
+#### Task Organizers
+* Benjamin Kille, TU Berlin, Germany (benjamin.kille (at) tu-berlin.de)
+* Andreas Lommatzsch, TU Berlin, Germany (andreas.lommatzsch (at) dai-labor.de)
+* Özlem Özgöbek, NTNU Trondheim, Norway
-#### Task organizers
-
-
-#### Task auxiliaries
-
+#### Task Auxiliaries
+* Martha Larson, Radboud University, Netherlands
#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
+* 31 July: Data release
+* ~~31 October~~ 16 November: Runs due + Start working on Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December : MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/no-audiospeech.md b/_editions/2020/tasks/no-audiospeech.md
deleted file mode 100644
index db1b048c6..000000000
--- a/_editions/2020/tasks/no-audiospeech.md
+++ /dev/null
@@ -1,62 +0,0 @@
----
-# static info
-layout: task
-year: 2020
-hide: true
-
-# required info
-title:
-subtitle:
-blurb:
----
-
-
-
-### Task Description
-
-#### Introduction
-
-
-#### New for 2020
-
-
-#### Target group
-
-
-#### Data
-
-
-#### Ground Truth
-
-
-#### Evaluation Methodology
-
-
-#### References and recommended reading
-
-
-### Big Picture of the Task
-
-#### Innovation
-
-
-#### Focus
-
-
-#### Risk management
-
-
-#### Task organization team
-
-
-#### Task organizers
-
-
-#### Task auxiliaries
-
-
-#### Task Schedule
-Data release: \\
-Run submission: \\
-Results returned: \\
-Working Notes paper deadline:
diff --git a/_editions/2020/tasks/noaudio-speech.md b/_editions/2020/tasks/noaudio-speech.md
new file mode 100644
index 000000000..bd08b0186
--- /dev/null
+++ b/_editions/2020/tasks/noaudio-speech.md
@@ -0,0 +1,104 @@
+---
+# static info
+layout: task
+year: 2020
+hide: false
+
+# required info
+title: "No-Audio Multimodal Speech Detection Task"
+subtitle:
+blurb: "Participants receive videos (top view) and sensor readings (acceleration and proximity) of people having conversations in a natural social setting and are required to detect speaking turns. No audio signal is available for use. The task encourages research on better privacy preservation during recordings made to study social interactions, and has the potential to scale to settings where recording audio may be impractical."
+---
+
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
+
+#### Task Description
+
+Task participants are provided with video of individuals participating in a conversation that was captured by an overhead camera. Each individual is also wearing a badge-like device, recording tri-axial acceleration.
+
+The goal of the task is to automatically estimate when the person seen in the video starts speaking, and when they stop speaking using these alternative modalities. In contrast to conventional speech detection, for this task, no audio is used. Instead, the automatic estimation system must exploit the natural human movements that accompany speech (i.e., speaker gestures, as well as shifts in pose and proximity).
+
+This task consists of two subtasks, with a new optional subtask:
+
+* **Unimodal classification:** Design and implement separate speech detection algorithms exploiting each modality separately: Teams must submit separate decisions for the wearable modality and for the video modality.
+
+* **Multimodal classification:** Design and implement a speech detection approach that integrates modalities. Teams must submit a multimodal estimation decision, using some form of early, late or hybrid fusion.
+
+* **Analysis of failure test cases (optional):** From previous editions, test subjects with lower performances compared to the mean have been discovered. In this sub-task, participants are encouraged to analyze these particular subjects and show or hypothesize about possible reasons for such low performances.
+
+Speaking predictions must be made for every second. However, it is left to the teams if they decide to use a different interval length and later interpolate or extrapolate to the second level.
+
+
+#### Motivation and Background
+
+An important but under-explored problem is the automated analysis of conversational dynamics in large unstructured social gatherings such as networking or mingling events. Research has shown that attending such events contributes greatly to career and personal success [7]. While much progress has been made in the analysis of small pre-arranged conversations, scaling up robustly presents a number of fundamentally different challenges.
+
+This task focuses on analysing one of the most basic elements of social behaviour: the detection of speaking turns. Research has shown the benefit of deriving features from speaking turns for estimating many different social constructs such as dominance, or cohesion to name but a few. Unlike traditional tasks that have used audio to do this, here the idea is to leverage the body movements (i.e. gestures) that are performed during speech production which are captured from video and/or wearable acceleration and proximity. The benefit of this is that it enables a more privacy-preserving method of extracting socially relevant information and has the potential to scale to settings where recording audio may be impractical.
+
+The relationship between body behaviour such as gesturing while speaking has been well-documented by social scientists [1]. Some efforts have been made in recent years to try and estimate these behaviours from a single body-worn triaxial accelerometer, hung around the neck [2,3]. This form of sensing could be embedded into a smart ID badge that could be used in settings such as conferences, networking events, or organizational settings. In other works, video has been used to estimate speaking status [4,5]. Despite these efforts, one of the major challenges has been in getting competitive estimation performance compared to audio-based systems. As yet, exploiting the multi-modal aspects of the problem is an under-explored area that will be the main focus of this challenge.
+
+
+#### Target Group
+
+This challenge is targeted at researchers in wearable devices, computer vision, signal and speech processing. The aim is to provide an entry-level task that has a clearly definable ground truth. There are many nuances to this problem that would enable this problem to be solved better if an intuition behind the behaviour is better understood. The problem could also be solved without this knowledge. The hope, however, is that this task will allow researchers who are not familiar with social signal processing to learn more about the problem domain; we have subsequent challenges in mind in later years that would become increasingly complex in terms of the social context and social constructs that are not so easily understood in terms of their social cue representation (e.g. personality, attraction, conversational involvement). The recommended readings for the challenge are [3,5,6]. Reading references [1,2,4] may provide additional insights on how to solve the problem.
+
+
+#### Data
+
+The data consists of 70 people who attended one of three separate mingle events (cocktail parties). Overhead camera data as well as wearable tri-axial accelerometer data for an interval of 30 minutes is available for this task. Each person used a wearable device (to record the acceleration acceleration) hung around the neck as a conference badge. A subset of this data will be kept as a test set. All the samples of this test set will be for subjects who are not in the training set.
+
+All the data is synchronized. The video data is mostly complete, with some segments missing as the participants can leave the recording area at any time (e.g. to go to the bathroom). The frame rate of the video and sample rate of the accelerometer data are captured at 20Hz. Note that due to the crowded nature of the events, there can be strong occlusions between participants in the video, which we hope to evaluate in one of our sub-tasks.
+
+
+#### Evaluation Methodology
+
+Manual annotations are provided for binary speaking status (speaking / non-speaking) for all people. These annotations are carried out for every frame in video (20 FPS). As mentioned above, speaking predictions must be made for every second.
+
+Since the classes are severely imbalanced, we will be using the Area Under the ROC Curve (ROC-AUC) as the evaluation metric. Thus, participants should submit non-binary prediction scores (posterior probabilities, distances to the separating hyperplane, etc.).
+
+The task will be evaluated using a subset of the data left as a test set. All the samples of this test set will be for subjects who are not present in the training set.
+
+For evaluation, we will ask the teams to provide the following estimations for the two subtasks states above (unimodal and multimodal):
+
+* **Person independent:** All samples are provided to the classifier together, irrespective of the subject that the samples came from. Note that the test samples we provide will samples taken from people who are not in the training data.
+
+* **(optional) Person specific:** Only samples generated from the same subject are provided to the classifier. So we expect participants to train one classifier for each person and output test results per person-specific classifier. This can be a useful sanity check as the performance of the method, which should, in theory, perform better when trained on a specific person rather than other people.
+The data contains occluded and non-occluded moments, and the performance of systems will be also calculated for both of these subsets individually, in order to gain greater insight into the results that participants achieve.
+
+
+#### References and recommended reading
+
+
+[1] McNeill, D.: [Language and gesture](https://www.cambridge.org/core/books/language-and-gesture/2D216A21B6484304C347FFB0DFCC39BB), vol. 2. Cambridge University Press (2000)
+
+[2] Hung, H., Englebienne, G., Kools, J.: [Classifying social actions with a single accelerometer](https://dl.acm.org/doi/pdf/10.1145/2493432.2493513?casa_token=rB1te_mCb3wAAAAA:dTnFrRm1YqAOVqsixGmJu_Xc1fKZQfhLbuju5meZnMMj1C15xzSQ0yBvnE5Nw3SFnSnXdC9ls3ZEC1M). In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 207–210. ACM (2013)
+
+[3] Gedik, E. and Hung, H., [Personalised models for speech detection from body movements using transductive parameter transfer](https://link.springer.com/article/10.1007/s00779-017-1006-4), Journal of Personal and Ubiquitous Computing, (2017)
+
+[4] Hung, H. and Ba, S. O., [Speech/non-speech Detection in Meetings from Automatically Extracted Low Resolution Visual Features](https://infoscience.epfl.ch/record/146060), Idiap Research Report, (2010)
+
+[5] Cristani, M., Pesarin, A., Vinciarelli, A., Crocco, M. , and Murino, V., [Look at who’s talking: Voice activity detection by automated gesture analysis](https://d1wqtxts1xzle7.cloudfront.net/8048683/gestures.pdf?1327801984=&response-content-disposition=inline%3B+filename%3DLook_at_whos_talking_Voice_activity_dete.pdf&Expires=1594664408&Signature=ea8pxw-LIng563aOFzxmlug-7SJqjNvizHJ1UY1kY-ANJ8qq8XS0~EBhOvKVaTT1KgAoducvgJHOdh7md3~jYFqBqcVV7QGsKRt8H5s1Ni0m7yOndhI5Acm6RAJzOUsHCubP3LsyzdClZ5sAP769KLVubpaweNw5uvUJzw8kbOTijVzF7rET4aOmc4FY7m0avFzi4jlYr65kJm5jIG1AOOfY7gycMbhYfJalg4n7C4H2X7Xyt-IqvDfHnpuxSK6Hj4pljfTn8wuFJjt6OTeDmA7jNlyiRMqhpuuvhhoK94N2~Zq1KFe6H4wDGH1BjWSGZwfwkBpL4J3J2BzGJdVCtw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA), In the workshop on Interactive Human Behavior Analysis in Open or Public Spaces, International Joint Conference on Ambient Intelligence, (2011).
+
+[6] Cabrera-Quiros, L., Demetriou, A., Gedik, E., van der Meij, L., & Hung, H. (2018). [The MatchNMingle dataset: a novel multi-sensor resource for the analysis of social interactions and group dynamics in-the-wild during free-standing conversations and speed dates](http://homepage.tudelft.nl/3e2t5/MatchNMingle.pdf). IEEE Transactions on Affective Computing.
+
+[7] Wolff, H.-G. and Moser, K. , [Effects of networking on career success: a longitudinal study](http://homepages.se.edu/cvonbergen/files/2013/01/Effects-of-Networking-on-Career-Success_A-Longitudinal-Study.pdf). Journal of Applied Psychology, 94(1):196, (2009).
+
+
+#### Task Organizers
+
+* Laura Cabrera Quiros, Instituto Tecnológico de Costa Rica, Costa Rica and Delft University of Technology, Netherlands (guest). l dot cabrera at itcr.ac.cr
+* Hayley Hung, Delft University of Technology, Netherlands. h dot hung at tudelft dot nl
+* Jose Vargas-Quiros, Delft University of Technology, Netherlands. J dot D dot VargasQuiros at tudelft dot nl
+
+
+
+#### Task Schedule
+* 21 July: Data release
+* ~~31 October~~ 16 November: Runs due + Start working on Working notes paper
+* ~~15 November~~ 23 November: Results returned
+* 30 November: Working notes paper
+* 11, 14-15 December : MediaEval 2020 Workshop (Fully online.)
+
+Workshop will be held online.
diff --git a/_editions/2020/tasks/pixelprivacy.md b/_editions/2020/tasks/pixelprivacy.md
index db1b048c6..0a2ef6cd5 100644
--- a/_editions/2020/tasks/pixelprivacy.md
+++ b/_editions/2020/tasks/pixelprivacy.md
@@ -2,61 +2,83 @@
# static info
layout: task
year: 2020
-hide: true
+hide: false
# required info
-title:
-subtitle:
-blurb:
+title: "Pixel Privacy: Quality Camouflage for Social Images"
+subtitle:
+blurb: In this task, participants develop adversarial approaches that camouflage the quality of images. A camouflaged image appears to be unchanged, or even enhanced, to the human eye. At the same time, the image will fool a Blind Image Quality Assessment algorithm into predicting that its quality is low. Quality camouflage will help to ensure that personal photos, e.g., vacation photos depicting people, are less easily findable via image search engines.
---
-
+
+*See the [MediaEval 2020 webpage](https://multimediaeval.github.io/editions/2020/) for information on how to register and participate.*
-### Task Description
+#### Task Description
+High-quality images shared online can be misappropriated for promotional goals. In this task, participants work to defeat an automatic image quality classifier, which effectively hides images.
+The camouflaged image appears to be appealing to the human eye, but the Blind Image Quality Assessment (BIQA) classifier finds it to be low quality, i.e., there is a dramatic descrease in the image's automatically predicted quality score.
-#### Introduction
+Participants will receive a set of images (representative of images shared on social media) and are required to modify them. The modification should achieve two goals: (1) Protection: It must block the ability of a binary BIQA classifier from correctly predicting the quality of images and (2) Appeal: The changes made to the image must be as imperceptible to the human eye as possible, or the changes must contribute to enhancing the appeal of the image.
+Note that the task is not focused on concealing sensitive information from humans, rather from automatic inference.
+This year the quality camouflage task is a "whitebox" attack. Participants' goal is to defeat a BIQA that predicts the perceptual quality of images. The BIQA is trained on KonIQ-10k, which contains 10,073 in-the-wild images annotated with subjective quality scores.
-#### New for 2020
+Participants can choose to address the task in one of two different ways. In the first, the quality camouflage approach seeks to make invisible changes to the image. In the second, the approach makes visible changes to the image, but restricts itself to changes that enhance the image’s appeal, or at least do not bother someone looking at the image.
+We encourage participants to release their code.
-#### Target group
+#### Background and Motivation
+Conventionally, adversarial images are studied in the context of scenarios in which they can cause harm by misleading computer vision system. *Pixel Privacy* introduces the notion that adversarial images can serve another goal as well: protect privacy-sensitive information in cases in which the computer vision system itself may be a source of harm. An important example is image search engines that can find images of people who are pictured in a specific location or setting. In the past installment of the task, we have investigated how adversarial images can effectively prevent the inference of semantic information concerning the scene of an image.
+Semantic information, is not, however, the only characteristic that a search engine uses in order to return a results list to a user. Another characteristic is automatically-inferred image quality. In the 2020 Pixel Privacy task we turn our attention to *quality camouflage*, i.e., approaches which can cause a Blind Image Quality Assessment (BIQA) to misclassify an image as low quality, when it appears to be high quality to the human eye. Quality camouflage will help to ensure that personal photos reflecting sensitive scene information, e.g., vacation photos depicting people, are less easily findable via image search engines.
-#### Data
-
-
-#### Ground Truth
+
+#### Target Group
+We hope that this task attracts a wide range of participants who are concerned about privacy from computer scientists to artists and photographers. Within the field of computer science, people interested in machine learning, adversarial machine learning, computer graphics, privacy, and computer vision will find the task interesting.
+#### Data
+Development data will be the KonIQ-10k data set. Test data will be selected from the validation set of the Places365, and different from previous editions, we will use high-resolution images for ensuring relatively high quality. Participants are provided with a set of correctly classified images that are also predicted to have ‘good quality’ by the BIQA model.
#### Evaluation Methodology
+The protection score will be the accuracy of the BIQA prediction on modified images by participants. We specify the threshold that is to be used by BIQA. Note that we expect that accuracy to decrease after protection, but theoretically it is also possible that protection fails, and that it stays the same.
+We appreciate approaches that are robust against practical post-processing operations. So the submitted images will be evaluated on their slightly JPEG compressed (quality factor = 90%) version. We have confirmed such compression has little impact on the BIQA scores.
-#### References and recommended reading
-
-
-### Big Picture of the Task
-
-#### Innovation
+Appeal will be evaluated by a jury of computer vision experts. Submissions will be ranked as follows: All approaches that achieve a protection score of at least 50% (50% reduction in the accuracy of the prediction) will be ranked in terms of their appeal by the jury.
+#### References and recommended reading
+
+
+Vlad Hosu, Hanhe Lin, Tamas Sziranyi and Dietmar Saupe. [KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment](https://ieeexplore.ieee.org/document/8968750). In IEEE Transactions on Image Processing 29, (Jan. 2020), 4041-4056.
-#### Focus
+Samuel Dodge, and Lina Karam. [Understanding How Image Quality Affects Deep Neural Networks](https://ieeexplore.ieee.org/document/7498955). In Proceedings of 8th International Conference on Quality of Multimedia Experience (QoMEX '16). IEEE, 1-6.
+Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.[Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572). International Conference on Learning Representations (ICLR '15).
-#### Risk management
+Pixel Privacy Task. 2018. Working Notes Proceedings of the MediaEval 2018 Workshop. Retrieved from http://ceur-ws.org/Vol-2283/
+Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, and Duen Horng Chau. 2018. [SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression](https://dl.acm.org/doi/10.1145/3219819.3219910). In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18). Association for Computing Machinery, New York, NY, USA, 196–204.
-#### Task organization team
+Zhengyu Zhao, Zhuoran Liu, and Martha Larson. 2020. [Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter](https://arxiv.org/abs/2002.01008). In Proceedings of the 31th British Machine Vision Conference (BMVC '20).
+
-#### Task organizers
+#### Task Organizers
+
+
+
+
+
+
+
+### Important Dates (Updated)
+* July-September 2021: Data releases
+* Mid-November 2021: Runs due (See individual task pages for the exact deadlines)
+* 29 November 2021: Working notes paper due
+* 9 December 2021: Video and workshop-ready paper due
+* 13-15 December 2021: MediaEval 2021 Workshop Online (The workshop will be held during the "Golden Hours" 14:00-18:30 UTC+1)
+
+#### The MediaEval Organization
+MediaEval is made possible by the efforts of a larger number of task organizers, who each are responsible for organizing their own tasks. Please see the individual task pages for their name. The over all organization is carried out by the MediaEval Coorindation Committe and guided by the Community Council.
+
+##### The MediaEval Coordination Committee (2021)
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Steven Hicks, SimulaMet, Norway
+* Martha Larson, Radboud University, Netherlands (Overall coordinator and main contact person)
+
+##### Special Thanks to
+* Ngoc-Thanh Nguyen, University of Information Technology, Vietnam
+* Ricardo Manhães Savii, Dafiti Group, Brasil (Website)
+
+##### The MediaEval Community Council (2021)
+* Martha Larson, Radboud University, Netherlands (Coordinator and contact person)
+* Gareth J. F. Jones, Dublin City University, Dublin, Ireland
+* Bogdan Ionescu, University Politehnica of Bucharest, Romania
+
+MediaEval is grateful for the support of [ACM Special Interest Group on Multimedia](http://sigmm.org/)
+
+
diff --git a/_editions/2021/tasks/wellbeing.md b/_editions/2021/tasks/wellbeing.md
new file mode 100644
index 000000000..43263fa06
--- /dev/null
+++ b/_editions/2021/tasks/wellbeing.md
@@ -0,0 +1,110 @@
+---
+# static info
+layout: task
+year: 2021
+hide: false
+
+# required info
+title: "Insight for Wellbeing: Cross-Data Analytics for (transboundary) Haze Prediction"
+subtitle:
+blurb: "The task is organized as multiple subtasks to encourage multi-disciplinary research to consider additional data sources (cross-data) to improve prediction and/or find insights for wellbeing based on environmental factors, satellite remote sensing, social/news data, etc. The problems this task tries to tackle are \"air pollution\" and \"transboundary haze\"."
+---
+
+
+*See the [MediaEval 2021 webpage](https://multimediaeval.github.io/editions/2021/) for information on how to register and participate.*
+
+#### Task Description
+Task participants will work on developing models for transboundary haze prediction using timeseries data describing air pollution concentration changes over time, weather data, and other related data sources, which are recorded at multiple locations across countries in the [ASEAN](https://en.wikipedia.org/wiki/ASEAN) region.
+
+
+Haze air pollution describes the pollution consisting of particulate matter of smoke, dust, and other vapours present in the air, which originate from large-scale forest and land fires, factories, and cars. When this mixture of air-borne pollutants reaches high levels it causes many health problems (respiratory infections, cardiovascular and lung diseases) especially among people who are already ill, and has negative impacts on visibility, economic production, transportation, and tourism. When haze pollution remains measurable at high levels after crossing into another country’s air space, it is referred to as a transboundary haze problem.
+
+
+The task is organized as multiple subtasks to encourage multi-disciplinary research that can consider additional data sources (cross-data) to improve prediction and/or find insights based on environmental factors, satellite remote sensing, social/news data, etc.
+
+Participants in this task will tackle three subtasks:
+
+*Subtask 1: 3-Day Localized Air Pollution Prediction:* in this subtask, the objective is to predict the PM10 value at different locations in multiple countries using data only from each country itself. This subtask will explore the accuracy of predicting air pollution for a few days ahead (currently 3), and it is designed to evaluate how well this objective can be achieved if each country depends only on its own direct weather data.
+
+*Subtask 2: 3-Day Transboundary Air Pollution Prediction:* the objective in this subtask is to predict the PM10 value in multiple countries by considering other data sources available from the same country or neighboring countries (remote sensing, social media streams, news, etc.). This subtask is encouraged to address transboundary haze effects, for example by observing the improvements of prediction accuracy once the haze and weather situation (e.g. wind/fire information) in neighboring countries is taken into account, or through other insights and conclusions that the participants find.
+
+*Subtask 3: Transfer Learning:* in this subtask, participants will re-visit subtasks 1 or 2 above, by considering transfer learning techniques in their solutions using pre-trained models for predicting new data, or by using models from subtask-1 in subtask-2, etc. The application of transfer learning can demonstrate that certain patterns learnt from other regions’ data sources or models (eg. via access to larger datasets) help in improving predictions for different regions (eg. where data is scarce or less granular).
+
+
+#### Motivation and background
+In the past decade, many countries especially in the ASEAN region have reported increased probabilities of cardiovascular and respiratory diseases, and haze pollution will affect patients with such diseases, and will have a negative impact on the well-being of citizens and tourists, social and economic activities. The accurate prediction of the haze situation for multiple time steps (hours or days) can help personal and public health advice and decision making (e.g. planning outdoor activities, closure of schools) in the event of predicted prolonged dangerous levels of haze. Tapping into multiple sources from ground instruments, remote sensing mechanisms, and other data streams, in addition to improved modeling techniques can help scientists track pollutants spread and provide better forecasting systems.
+
+Transboundary haze problem refers to the situation where haze originating at one country or region remains at high levels after crossing into other countries, resulting a recurrent issue in many regions in the world, especially in Southeast Asia where the sources contributing to haze pollution differ at each country with varying percentages coming from localized or transboundary sources. For example, transboundary (haze) pollution episodes are often attributed to the long-range transport of biomass fires from slash-and-burn activities during dry seasons or from forest fires, which travel depending on weather conditions to affect several neighboring countries.
+
+Particulate matter concentrations (PM10, PM2.5) and other gases concentrations are usually used to calculate air quality index measures that describe pollutant severity. Past recorded data of air pollution and meteorological parameters have been used by researchers and practitioners from academia and government agencies to develop air pollution prediction models to forecast changes in air pollution. Some studies used mathematical statistical models [1] to make long-term predictions of particulate matter by using climate models or satellite remote sensing. Besides, several machine learning methods [2] were used to improve accuracy in short-term prediction with the inclusion of meteorological data. Deep learning techniques, such as hybrid of convolutional neural network and long short-term memory methods were used, as proposed in [3,4] by Zhao and Zettsu on air pollution data from Japan, and by Yang et al. in 2020 [5] to predict hourly particulate matter concentrations in South Korea. Comparisons among multiple methods on daily air pollution prediction in Brunei were presented in 2020 [6] by Aziz et. al.
+
+#### Target group
+This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, data science, event-based processing and analysis, urban computing, environmental and atmospheric sciences.
+
+#### Data
+The data that is provided will include daily readings of PM10 concentrations recorded by multiple weather stations in ASEAN countries (Brunei, Singapore, and Thailand), covering different periods between 2010 and 2019 . The data also includes atmospheric parameters of daily (and in some cases hourly) values of temperature, rainfall, humidity, and wind speed/direction. However, not all parameters are available at every station/country, with some missing values in the data, which add to the challenge of this task.
+
+All data will be provided in CSV format, without inclusion of any Personal data. Task participants must sign the agreement for using the data prepared by MediaEval and NICT/UTB, which limit the use of the data for this competition only.
+
+#### Evaluation methodology
+Participants are encouraged to participate in all three subtasks described above. However, it is still accepted to submit solutions for only one or two of them (any subtask). There will be two types of prizes: most accurate models (marks will be assigned and averaged over all subtasks); and most innovative out-of-the-box idea (across subtasks as well).
+
+There will be two timeseries datasets, one for training and one for testing, for each country. Separate data files will include air pollution readings and weather parameters, which are time stamped and with location information.
+
+The testing datasets will be created from the ending tail of the timeseries data in each station/country, and will have some values hidden (multiple 3-day windows) which the participants will need to predict and use as their submissions for the two involved subtasks. The predicted values will be compared with the (kept aside) ground truth values, where the metrics of root mean squared error (RMSE), symmetric mean absolute percentage error (SMAPE), and mean absolute error (MAE) will be calculated and averaged for all stations in each country.
+
+Accurate predictions are supposed to have the smallest possible error values on each of the metrics across all stations in a single country. Participants may opt to use the same or different methods for different countries in their predictions. It is expected that the models should be accurate across countries as well, so the average of the errors over countries will be computed as a general indicator. However, individual accurate models per country are the focus.
+It is to be emphasized that for subtask-1 (localized air pollution), participants should use only the data from each specific country when developing their models and performing test data predictions. However, for subtask-2 and subtask-3, participants are expected to utilize all available data (across countries), and potentially other data sources that help in capturing the transboundary nature of the problem and provide better predictions.
+
+At the end of the challenge, participants are expected to submit their source code and any other necessary resources, together with their testing files predictions, in order to validate the applied methodologies.
+
+
+#### References and recommended reading
+
+
+[1] Aaron van Donkelaar, Randall V. Martin, Michael Brauer, Ralph Kahn, Robert Levy, Carolyn Verduzco, and Paul J. Villeneuve. 2010. [Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898863/), Environment Health Perspectives, 118(6), 847-855.
+
+[2] Sharjil Saeed, Lal Hussain, Imtiaz Ahmed Awan, and Adnan Idris. 2017. [Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours](http://paper.ijcsns.org/07_book/201711/20171106.pdf), International Journal of Computer Science and Network Security, 17(11), 45–52.
+
+[3] Peijiang Zhao and Koji Zettsu. 2018. [Convolution recurrent neural networks for short-term prediction of atmospheric sensing data](https://ieeexplore.ieee.org/document/8726777), 2018 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 30th July–3rd August 2018, Halifax, NS, Canada.
+
+[4] Peijiang Zhao and Koji Zettsu. 2019. [Convolution recurrent neural networks based dynamic transboundary air pollution prediction](https://ieeexplore.ieee.org/document/8712835), 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 15th March–18th March 2019, Suzhou, China.
+
+[5] Guang Yang, HwaMin Lee, and Giyeol Lee. 2020. [A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea](https://www.mdpi.com/2073-4433/11/4/348), Atmosphere 2020, 11(4), 348–367.
+
+[6] Effa Nabilla Aziz, Asem Kasem, Wida Susanty Haji Suhaili, and Peijiang Zhao. 2021. [Convolution Recurrent Neural Network for Daily Forecast of PM10 Concentrations in Brunei Darussalam](https://www.aidic.it/cet/21/83/060.pdf). Journal of Chemical Engineering Transactions, 2021, Vol. 83, pp. 355–360.
+
+[7] Minh-Son Dao, Peijiang Zhao, Tomohiro Sato, Koji Zettsu, Duc-Tien Dang-Nguyen, Cathal Gurrin, Ngoc-Thanh Nguyen. 2019. [Overview of MediaEval 2019: Insights for Wellbeing Task Multimodal Personal Health Lifelog Data Analysis](http://ceur-ws.org/Vol-2670/MediaEval_19_paper_10.pdf).
+
+[8] Peijiang Zhao, Minh-Son Dao, Thanh Nguyen, Thanh-Binh Nguyen, Duc-Tien Dang-Nguyen, and Cathal Gurrin. 2020. [Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis](https://www.eigen.no/MediaEval_20_paper_11.pdf).
+
+[9] Dat Q. Duong, Quang M. Le, Tan-Loc Nguyen-Tai, Dong Bo, Dat Nguyen, Minh-Son Dao, and Binh T. Nguyen. 2020. [Multi-source Machine Learning for AQI Estimation](https://ieeexplore.ieee.org/document/9378322), IEEE Big Data 2020.
+
+[10] Peijiang Zhao and Koji Zettsu. 2020. [MASTGN: Multi-Attention Spatio-Temporal Graph Networks for Air Pollution Prediction](https://ieeexplore.ieee.org/document/9378156), IEEE Big Data 2020.
+
+[11] Phuong-Binh Vo, Trong-Dat Phan, Minh-Son Dao, and Koji Zettsu. 2019. [Association Model between Visual Feature and AQI Rank Using Lifelog Data]( https://ieeexplore.ieee.org/abstract/document/9005636), IEEE Big Data 2019.
+
+[12] Dona Rofithoh Don Ramli and Rugayah Hashim. 2020. [National Interest Versus Regional Interest: The Case of Transboundary Haze Pollution](https://link.springer.com/chapter/10.1007/978-981-15-3859-9_12). Charting a Sustainable Future of ASEAN in Business and Social Sciences, pp 123-132.
+
+
+#### Task organizers
+* Asem Kasem, Universiti Teknologi Brunei (UTB), Brunei Darussalam, asem(dot)kasem(at)utb(dot)edu(dot)bn
+* Minh-Son Dao, National Institute of Information and Communications Technology, Japan (NICT), dao(at)nict(dot)go(dot)jp
+* Ngoc-Thanh Nguyen, Western Norway University of Applied Sciences, Norway (HVL), ntng(at)hvl(dot)no
+* Duc-Tien Dang-Nguyen, University of Bergen, Norway (UiB)
+* Cathal Gurrin, Dublin City University, Ireland (DCU)
+* Tran Minh Triet, University of Science, Vietnam (HCMUS)
+* Nguyen Thanh Binh, University of Science, Vietnam (HCMUS)
+* Wida Suhaili, Universiti Teknologi Brunei (UTB)
+
+#### Task Schedule (Updated)
+* 01 July: Data release
+* 14 November: Runs due + Start writing working notes paper
+* 19 November: Results returned
+* 29 November: Working notes paper
+* 13-15 December: MediaEval 2021 Workshop
+
+
+
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+---
+layout: edition
+title: MediaEval 2022
+year: 2022
+permalink: /editions/2022/
+---
+
+The MediaEval Multimedia Evaluation benchmark offers challenges in artificial intelligence for multimedia data. Participants address these challenges by creating algorithms for retrieval, analysis, and exploration. Solutions are systematically compared using a common evaluation procedure, making it possible to establish the state of the art and track progress. Our larger aim is to promote reproducible research that makes multimedia a positive force for society.
+
+MediaEval goes beyond other benchmarks and data science challenges in that it also pursues a “Quest for Insight” (Q4I). With Q4I we push beyond only striving to improve evaluation scores to also working to achieve deeper understanding about the challenges. For example, properties of the data, strengths and weaknesses of particular types of approaches, and observations about the evaluation procedure.
+
+The MediaEval 2022 Workshop will be held 12-13 January 2023, collocated with [MMM 2023](https://www.mmm2023.no) in Bergen, Norway and also online. For the preliminary workshop proceedings and the workshop schedule, please see the [MediaEval Workshop Information Announcement](https://multimediaeval.github.io/2023/01/06/workshop-information.html)
+
+
+
+
+
+
+
+
+### Workshop
+
+
+* Workshop schedule is here: [MediaEval 2022 Workshop Program](https://multimediaeval.github.io/editions/2022/docs/MultimediaEval_2022_Detailed_Program.pdf)
+* Proceedings: [MediaEval 2022 Working Notes Proceedings](https://ceur-ws.org/Vol-3583/)
+
+Workshop group photo:
+
+
+
+### Task schedule
+
+* June 2022: Sign up opens for participation in MediaEval 2022
+* June-August 2022: Data releases
+* Beginning November 2022: Runs due (see task pages for the deadline for each task)
+* 28 November 2022: Working notes paper due
+* 12-13 January 2023: MediaEval 2022 Workshop, Collocated with [MMM 2023](https://www.mmm2023.no) in Bergen, Norway and also online.
+
+##### The MediaEval Coordination Committee (2022)
+* Mihai Gabriel Constantin, University Politehnica of Bucharest, Romania
+* Steven Hicks, SimulaMet, Norway
+* Martha Larson, Radboud University, Netherlands (Overall coordinator and main contact person)
+
+MediaEval is grateful for the support of [ACM Special Interest Group on Multimedia](http://sigmm.org/)
+
+