This project contains three landmark detection algorithms, implemented in PyTorch.
The training and testing codes for SAN (CVPR 2018) are located in the SAN directory.
The training and testing codes for Supervision-by-Registration (CVPR 2018) are located in the SBR directory.
The model codes for Teacher Supervises Students (TS3) (ICCV 2019) are located in the TS3 directory.
The training and testing codes for SRT (TPAMI) 2020 are located in the SRT directory.
If this project helps your research, please cite the following papers:
@inproceedings{dong2018san,
title={Style Aggregated Network for Facial Landmark Detection},
author={Dong, Xuanyi and Yan, Yan and Ouyang, Wanli and Yang, Yi},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={379--388},
doi={10.1109/CVPR.2018.00047},
year={2018}
}
@inproceedings{dong2018sbr,
title={{Supervision-by-Registration}: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors},
author={Dong, Xuanyi and Yu, Shoou-I and Weng, Xinshuo and Wei, Shih-En and Yang, Yi and Sheikh, Yaser},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={360--368},
doi={10.1109/CVPR.2018.00045},
year={2018}
}
@inproceedings{dong2019teacher,
title={Teacher Supervises Students How to Learn from Partially Labeled Images for Facial Landmark Detection},
author={Dong, Xuanyi and Yang, Yi},
booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
pages={783--792},
doi={10.1109/ICCV.2019.00087},
year={2019}
}
@inproceedings{dong2020srt,
title = {Supervision by Registration and Triangulation for Landmark Detection},
author = {Dong, Xuanyi and Yang, Yi and Wei, Shih-En and Weng, Xinshuo and Sheikh, Yaser and Yu, Shoou-I},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
volume = {},
number = {},
keywords = {Landmark Detection;Optical Flow;Triangulation;Deep Learning},
doi = {10.1109/TPAMI.2020.2983935},
ISSN = {1939-3539},
year = {2020},
month = {},
note = {\mbox{doi}:\url{10.1109/TPAMI.2020.2983935}}
}
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