ZacharyWang-007 / FED-Occluded-ReID

This is the Pytorch implementation of Feature Erasing and Diffusion Network for Occluded Person Re-Identification (CVPR2022).

Feature Erasing and Diffusion Network for Occluded Person Re-Identification (CVPR2022)

Pytorch implementation for the occluded person reid algorithm described in the paper Feature Erasing and Diffusion Network for Occluded Person Re-Identification (CVPR2022)

Pipline

Experiment Results on Holistic and Occluded Person ReID Datasets

Retrieve Comparison between TransReID

Requirements

Installation

Please refer to TransReID

Dataset Preparation

Please download Occluded-Duke dataset and cropped patches. Meanwhile place cropped patches into Occluded-Duke (just because of dataloader).

Pretrained Model Preparison

Please download pretrained ViT backbone in advance.

Model training and testing

before training and testing, please update config file accordingly. Around 13G GPU memory is required.

  python train.py 

Citation

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}
}

Contact

If you have any question, please feel free to contact us. E-mail: [e-mail]