only focus on video person re-identification
| No. | Dataset | Rank1 | mAP | Link |
|---|---|---|---|---|
| 1 | PRID | 97.4% | Null | paper |
| 2 | iLIDS-VID | 92.0% | Null | paper[ICCV2021] |
| 3 | Mars | 91.4% | 86.7% | paper[CVPR2021] |
| 4 | DukeMTMC | 98.3% | 97.4% | paper[ICCV2021] |
| 5 | LS-VID | 84.6% | 75.1% | paper[CVPR2021] |
please refer to LEADERBOARD.md for the leaderboard on public benchmarks.
If you find this repo useful, please kindly cite the following paper:
@inproceedings{liu2021watching,
title={Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification},
author={Liu, Xuehu and Zhang, Pingping and Yu, Chenyang and Lu, Huchuan and Yang, Xiaoyun},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13334--13343},
year={2021}
}
@article{liu2021video,
title={A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification},
author={Liu, Xuehu and Zhang, Pingping and Yu, Chenyang and Lu, Huchuan and Qian, Xuesheng and Yang, Xiaoyun},
journal={arXiv preprint arXiv:2104.01745},
year={2021}
}