littlexinyi/mgcc

The code of MGCC: Text-based Occluded Person Re-identification via Multi-Granularity Contrastive Consistency Learning

【AAAI 2024 】Text-based Occluded Person Re-identification via Multi-Granularity Contrastive Consistency Learning

This repository offers the official implementation of MGCC in PyTorch.

Overview

Requirements

• PyTorch version = 1.7.1

• Install other libraries via

pip install -r requirements.txt

Data preparation

• CUHK-PEDES

  Download the CUHK-PEDES dataset from here

  Organize them in ./dataset/CUHK-PEDES/ folder as follows:

  |-- dataset/
  |   |-- CUHK-PEDES/
  |       |-- imgs
              |-- cam_a
              |-- cam_b
              |-- ...
  |       |-- CUHK-PEDES.json
  |-- others/
  

• ICFG-PEDES

  Download the ICFG-PEDES dataset from here

  Organize them in ./dataset/ICFG-PEDES/ folder as follows:

  |-- dataset/
  |   |-- ICFG-PEDES/
  |       |-- imgs
              |-- test
              |-- train 
  |       |-- ICFG_PEDES.json
  |-- others/
  

• RSTPReid

  Download the RSTPReid dataset from here

  Organize them in ./dataset/RSTPReid/ folder as follows:

  |-- dataset/
  |   |-- RSTPReid/
  |       |-- imgs
  |       |-- RSTPReid.json
  |-- others/
  

• Occlusion Instance Augmentation

  After changing the parameters of parse_args fuction in process_data.py according to different datasets, run the process_data.py in the dataset folder.

How to Run

• About the pretrained CLIP and Bert checkpoints

  Download the pretrained CLIP checkpoints from here and save it in path ./src/pretrain/clip-vit-base-patch32/

  Download the pretrained Bert checkpoints from here and save it in path ./src/pretrain/bert-base-uncased/

• About the running scripts

  Use CUHK-PEDES as examples:

  sh experiment/CUHK-PEDES/train.sh
  

  After training done, you can test your model by run:

  sh experiment/CUHK-PEDES/test.sh
  

  As for the usage of different parameters, you can refer to src/option/options.py for the detailed meaning of each parameter.

Citation

If you find our method useful in your work, please consider staring 🌟 this repo and citing 📑 our paper:

@inproceedings{wu2024text,
  title={Text-based Occluded Person Re-identification via Multi-Granularity Contrastive Consistency Learning},
  author={Wu, Xinyi and Ma, Wentao and Guo, Dan and Zhou, Tongqing and Zhao, Shan and Cai, Zhiping},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={6},
  pages={6162--6170},
  year={2024}
}

Acknowledgments

The implementation of our paper relies on resources from SSAN, CLIP and XCLIP. We thank the original authors for their open-sourcing.

mgcc's Issues

Parameter setting

Thank you for your great work! How to set parameters in options.py(like IDloss) to reproduce the results in the paper？

About the paper appendix

Thanks for your contributions! I am wondering where can I get the appendix of this paper? ~~~///(^v^)\~~~