Pytorch implementation for the occluded person reid algorithm described in the paper Feature Erasing and Diffusion Network for Occluded Person Re-Identification (CVPR2022)
Retrieve Comparison between TransReID
Please refer to TransReID
Please download Occluded-Duke dataset and cropped patches. Meanwhile place cropped patches into Occluded-Duke (just because of dataloader).
Please download pretrained ViT backbone in advance.
before training and testing, please update config file accordingly. Around 13G GPU memory is required.
python train.py
If you find this code useful for your research, please cite our paper
@inproceedings{wang2022feature,
title={Feature Erasing and Diffusion Network for Occluded Person Re-Identification},
author={Wang, Zhikang and Zhu, Feng and Tang, Shixiang and Zhao, Rui and He, Lihuo and Song, Jiangning},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={4754--4763},
year={2022}
}
If you have any question, please feel free to contact us. E-mail: zhikang.wang@monash.edu