[ICME 2024 (Oral)] Color Space Learning for Cross-Color Person Re-identification

The official implementation of "Color Space Learning for Cross-Color Person Re-identification".

More detailed information is in the PAPER.

Authors: Jiahao Nie, Shan Lin, Alex C. Kot

Dataset

Visible & Infrared Person ReID (VI-ReID)

Please download and prepare SYSU-MM01 and RegDB datasets following AGW's guidance.

Please download our NTU-Corridor dataset from the link will be provided soon.

Cloth-Changing Person ReID (CC-ReID)

Please download PRCC dataset from their official repo.

Please download LTCC dataset from their lab website.

File Organization

    CSL/                      # project file
    ├── CC-ReID/              # code for CC-ReID
    ├── PRCC/                 # PRCC dataset
    |   ├── LTCC/             # LTCC dataset
    |   └── ...                                         
    |    
    ├── SYSU-MM01/            # SYSU-MM01 dataset
    ├── RegDB/                # RegDB dataset
    ├── NTU-Corridor/         # NTU-Corridor dataset
    └── ...

Performance

VI-ReID Datasets	Setting	Rank1	mAP	Setting	Rank1	mAP
SYSU-MM01 (All-Search)	NIR to RGB	72.5%	68.0%	RGB to NIR	73.5%	72.4%
SYSU-MM01 (Indoor-Search)	NIR to RGB	80.2%	82.9%	RGB to NIR	78.5%	76.9%
NTU-Corridor	NIR to RGB	83.4%	64.9%	RGB to NIR	86.2%	63.7%
RegDB	Thermal to RGB	85.8%	77.8%	RGB to Thermal	86.2%	77.9%

CC-ReID Datasets	Rank1	mAP
LTCC	56.2%	22.2%
PRCC	56.4%	56.0%

Training

Train a model by

python train_ext.py --dataset sysu --lr 0.1 --method adp --augc 1 --rande 0.5 --alpha 1 --square 1 --gamma 1 --gpu 1

• --dataset: which dataset "sysu" or "ntu" or "regdb".

• --lr: initial learning rate.

• --gpu: which gpu to run.

You may need mannully define the data path first.

Citation

If you use this codebase for your research, please consider citing:

 @article{nie2024cross,
  title={Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining},
  author={Nie, Jiahao and Xing, Yun and Zhang, Gongjie and Yan, Pei and Xiao, Aoran and Tan, Yap-Peng and Kot, Alex C and Lu, Shijian},
  journal={arXiv preprint arXiv:2401.08407},
  year={2024}
}

Acknowledgement

Our codebase is built based on AGW and CAJ's official code.

Reference

[1] A. Wu, W.-s. Zheng, H.-X. Yu, S. Gong, and J. Lai. Rgb-infrared crossmodality person re-identification. ICCV, 2017.

[2] D. T. Nguyen, H. G. Hong, K. W. Kim, and K. R. Park. Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors, 2017.

[3] Q. Yang, A. Wu, and W.-S. Zheng. Person re-identification by contour sketch under moderate clothing change. TPAMI, 2019.

[4] X. Qian, W. Wang, L. Zhang, F. Zhu, Y. Fu, T. Xiang, Y.-G. Jiang, and X. Xue. Long-term cloth-changing person re-identification. ACCV, 2020.

[5] M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C. Hoi. Deep learning for person re-identification: A survey and outlook. TPAMI, 2021.

[6] M. Ye, W. Ruan, B. Du, and M. Z. Shou. Channel augmented joint learning for visible-infrared recognition. ICCV, 2021.