Description
This is a modified version of a forked densecrf, which was used as a part of the DeepLab.
For more details about the inference algorithm used in this version, please refer to and consider citing the following paper:
@article{baque2015principled,
title={Principled Parallel Mean-Field Inference for Discrete Random Fields},
author={Baqu{\'e}, Pierre and Bagautdinov, Timur and Fleuret, Fran{\c{c}}ois and Fua, Pascal},
journal={arXiv preprint arXiv:1511.06103},
year={2015}
}
If you are using densecrf, please consider citing the following paper:
@inproceedings{KrahenbuhlK11,
title={Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials},
author={Philipp Kr{\"{a}}henb{\"{u}}hl and Vladlen Koltun},
booktitle={NIPS},
year={2011}
}
If you are using DeepLab, please consider citing following paper:
@article{papandreou15weak,
title={Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation},
author={George Papandreou and Liang-Chieh Chen and Kevin Murphy and Alan L Yuille},
journal={arxiv:1502.02734},
year={2015}
}
Building and Dependencies
You should have matio library installed.
To build the binary, just run make
.
Usage
... to be filled in ...
For the complete pipeline for semantic segmentation, please refer to DeepLab.
For the details (parameters) specific to this version, refer to
refine_pascal_nat/dense_inference.cpp
.