Public repository containing implementations of the work done in "A Projected Gradient Descent Method for CRF Inference Allowing End-to-End Training of Arbitrary Pairwise Potentials" and "Revisiting Deep Structured Models in Semantic Segmentation with Gradient-Based Inference". The latter is not yet published but can be found as a part of "End-to-End Learning of Deep Structured Models for Semantic Segmentation".
This repository also uses code from this repo that implements "Learning sparse high dimensional filters : Image Filtering, Dense CRFs and Bilateral Neural Networks".
The different types of CRF models presented in the paper are implemented as caffe layers. For example usage see the crfgd_tools folder. This folder contains code for data handling, training and result visualization.
Please consider citing the following publications if it helps your research:
@PhdThesis{crfe2e2018,
author = {Larsson, M{\aa}ns},
title = {End-to-End Learning of Deep Structured Models for Semantic Segmentation},
school = {Chalmers University of Technology (CTH), Gothenburg, Sweden},
year = {2018},
type = {Licentiate Thesis},
month = {Mar.}
}
@article{larsson2018revisiting,
title={Revisiting Deep Structured Models for Pixel-Level Labeling with Gradient-Based Inference},
author={Larsson, M{\aa}ns and Arnab, Anurag and Zheng, Shuai and Torr, Philip and Kahl, Fredrik},
journal={SIAM Journal on Imaging Sciences},
volume={11},
number={4},
pages={2610--2628},
year={2018},
publisher={SIAM}
}
@InProceedings{crfgd2018,
author="Larsson, M{\aa}ns and Arnab, Anurag and Kahl, Fredrik and Zheng, Shuai and Torr, Philip", editor="Pelillo, Marcello and Hancock, Edwin",
title="A Projected Gradient Descent Method for CRF Inference Allowing End-to-End Training of Arbitrary Pairwise Potentials",
booktitle="Energy Minimization Methods in Computer Vision and Pattern Recognition",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="564--579"
}
Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.
Please cite Caffe in your publications if it helps your research:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}