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https://github.com/d-x-y/landmark-detection
Four landmark detection algorithms, implemented in PyTorch.
https://github.com/d-x-y/landmark-detection
facial-landmarks keypoint-detection landmark-detection pytorch style-transfer
Last synced: about 3 hours ago
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Four landmark detection algorithms, implemented in PyTorch.
- Host: GitHub
- URL: https://github.com/d-x-y/landmark-detection
- Owner: D-X-Y
- License: mit
- Created: 2018-03-11T07:04:12.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-01-26T03:04:35.000Z (almost 4 years ago)
- Last Synced: 2024-11-06T00:02:51.528Z (7 days ago)
- Topics: facial-landmarks, keypoint-detection, landmark-detection, pytorch, style-transfer
- Language: Python
- Homepage: https://xuanyidong.com/assets/projects/TPAMI-2020-SRT.html
- Size: 21.8 MB
- Stars: 922
- Watchers: 33
- Forks: 180
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md
Awesome Lists containing this project
README
# Landmark Detection
This project contains three landmark detection algorithms, implemented in [PyTorch](pytorch.org).
- Style Aggregated Network for Facial Landmark Detection, CVPR 2018
- Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors, CVPR 2018
- Teacher Supervises Students How to Learn from Partially Labeled Images for Facial Landmark Detection, ICCV 2019
- Supervision by Registration and Triangulation for Landmark Detection, TPAMI 2020
## Style Aggregated Network for Facial Landmark Detection
The training and testing codes for [SAN (CVPR 2018)](https://xuanyidong.com/publication/cvpr-2018-san/) are located in the [SAN directory](https://github.com/D-X-Y/landmark-detection/tree/master/SAN).
## Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
The training and testing codes for [Supervision-by-Registration (CVPR 2018)](https://xuanyidong.com/publication/cvpr-2018-sbr/) are located in the [SBR directory](https://github.com/D-X-Y/landmark-detection/tree/master/SBR).
## Teacher Supervises Students How to Learn from Partially Labeled Images for Facial Landmark Detection
The model codes for [Teacher Supervises Students (TS3) (ICCV 2019)](https://arxiv.org/abs/1908.02116) are located in the [TS3 directory](https://github.com/D-X-Y/landmark-detection/tree/master/TS3).
## Supervision by Registration and Triangulation for Landmark Detection
The training and testing codes for [SRT (TPAMI) 2020](https://ieeexplore.ieee.org/document/9050873) are located in the [SRT directory](https://github.com/D-X-Y/landmark-detection/tree/master/SRT).
## Citation
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}}
}
```
## Contact
To ask questions or report issues, please open an issue on the [issues tracker](https://github.com/D-X-Y/landmark-detection/issues).