Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning

Dataset Preprocessing

Convert the dataset format (like Market1501).

python prepare_sysu.py   # for SYSU-MM01
python prepare_regdb.py  # for RegDB

You need to change the file path in the prepare_sysu(regdb).py.

Note: a pre-processed dataset can be downloaded from Baidu Netdisk (Password: ReID) or Google Drive.

Training

./train_sysu.sh   # for SYSU-MM01
./train_regdb.sh  # for RegDB

Two training stages are included and you need to specify the training stage by commenting another stage's main_worker like this:

main_worker_stage1(args,log_s1_name) # Stage 1
# main_worker_stage2(args,log_s1_name,log_s2_name) # Stage 2

Update: We optimized the code to make the training more stable. In the 2nd stage of training, we recommend setting use_hard to True, referring to [1].

Test

./test_sysu.sh    # for SYSU-MM01
./test_regdb.sh   # for RegDB

Citation

@InProceedings{Wu_2023_CVPR,
    author    = {Wu, Zesen and Ye, Mang},
    title     = {Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {9548-9558}
}

Our trained models can be downloaded here.

[1] Dai, Zuozhuo, et al. "Cluster contrast for unsupervised person re-identification." Proceedings of the Asian conference on computer vision. 2022.

Contact

zesenwu@whu.edu.cn

The code is implemented based on ClusterContrast and ADCA.