Salient-to-Broad Transition for Video Person Re-identification (CVPR 2022)
SANet: Statistic Attention Network for Video-Based Person Re-Identification (TCSVT 2021)
# Train
python main.py \
--arch ${sinet, sbnet, idnet, sanet} \
--dataset ${mars, lsvid, ...} \
--root ${path of dataset} \
--gpu_devices 0,1 \
--save_dir ${path for saving modles and logs} \
# Test with all frames
python main.py \
--arch ${sinet, sbnet, idnet, sanet} \
--dataset mars \
--root ${path of dataset} \
--gpu_devices 0,1 \
--save_dir ${path for saving logs} \
--evaluate --all_frames --resume ${path of pretrained model}| Methods | Paper | Reproduce | Download |
|---|---|---|---|
| SBNet (ResNet50 + SBM) | 85.7/90.2 | 85.6/90.7 | model |
| IDNet (Resnet50 + IDM) | 85.9/90.5 | 85.9/90.4 | model |
| SINet (ResNet50 + SBM + IDM) | 86.2/91.0 | 86.3/90.9 | model |
| SANet (ResNet50 + SA Block) | 86.0/91.2 | 86.7/91.2 | model |
| Methods | Paper | Reproduce | Download |
|---|---|---|---|
| SBNet (ResNet50 + SBM) | 77.1/85.1 | 77.2/85.3 | model |
| IDNet (Resnet50 + IDM) | 78.0/86.2 | 78.2/86.0 | model |
| SINet (ResNet50 + SBM + IDM) | 79.6/87.4 | 79.9/87.2 | model |
If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.
@inproceedings{bai2022SINet,
title={Salient-to-Broad Transition for Video Person Re-identification},
author={Bai, Shutao and Ma, Bingpeng and Chang, Hong and Huang, Rui and Chen, Xilin},
booktitle={CVPR},
year={2022},
}
@ARTICLE{9570321,
author={Bai, Shutao and Ma, Bingpeng and Chang, Hong and Huang, Rui and Shan, Shiguang and Chen, Xilin},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={SANet: Statistic Attention Network for Video-Based Person Re-Identification},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TCSVT.2021.3119983}
}
This code is based on the implementations of AP3D.