ZhiyinShao-H/LGUR

Learning Granularity-Unified Representations for Text-to-Image Person Re-identification

This is the codebase for our ACM MM 2022 paper.

datasets
└── cuhkpedes
    ├── captions.json
    └── imgs
        ├── cam_a
        ├── cam_b
        ├── CUHK01
        ├── CUHK03
        ├── Market
        ├── test_query
        └── train_query
└──icfgpedes
    ├── ICFG-PEDES.json
    └── ICFG_PEDES
        ├── test
        └── train

Download DeiT-small weights

wget https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth

Process image and text datasets

python processed_data_singledata_CUHK.py
python processed_data_singledata_ICFG.py

Train

python train_mydecoder_pixelvit_txtimg_3_bert.py

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{shao2022learning,
  title={Learning Granularity-Unified Representations for Text-to-Image Person Re-identification},
  author={Shao, Zhiyin and Zhang, Xinyu and Fang, Meng and Lin, Zhifeng and Wang, Jian and Ding, Changxing},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  year={2022}
}