Live demo: http://crfasrnn.torr.vision
PyTorch version: http://github.com/sadeepj/crfasrnn_pytorch
Tensorflow/Keras version: http://github.com/sadeepj/crfasrnn_keras
This package contains code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demonstration based on this code won the Best Demo Prize at ICCV 2015. Our software is built on top of the Caffe deep learning library. The current version was developed by:
Sadeep Jayasumana, Shuai Zheng, Bernardino Romera Paredes, Anurag Arnab, and Zhizhong Su.
Supervisor: Philip Torr
Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of objects. Currently we have trained this model to recognize 20 classes. This software allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you can send them to us.
Why are we doing this? This work is part of a project to build augmented reality glasses for the partially sighted. Please read about it here: smart-specs.
For demo and more information about CRF-RNN please visit the project website: http://crfasrnn.torr.vision.
If you use this code/model for your research, please cite the following papers:
@inproceedings{crfasrnn_ICCV2015,
author = {Shuai Zheng and Sadeep Jayasumana and Bernardino Romera-Paredes and Vibhav Vineet and
Zhizhong Su and Dalong Du and Chang Huang and Philip H. S. Torr},
title = {Conditional Random Fields as Recurrent Neural Networks},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2015}
}
@inproceedings{higherordercrf_ECCV2016,
author = {Anurag Arnab and Sadeep Jayasumana and Shuai Zheng and Philip H. S. Torr},
title = {Higher Order Conditional Random Fields in Deep Neural Networks},
booktitle = {European Conference on Computer V