【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
              |-- ...
  |       |-- reid_raw.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
  |       |-- data_captions.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.