High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification (AAAI 2024)

1. Prepare the datasets.

• (1) SYSU-MM01 Dataset [1]: The SYSU-MM01 dataset can be downloaded from this website.

• run python pre_process_sysu.py to pepare the dataset, the training data will be stored in ".npy" format.

• (2) RegDB Dataset [2]: The RegDB dataset can be downloaded from this website by submitting a copyright form.

• (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).

• A private download link can be requested via sending an email to [email protected].

• (3) LLCM Dataset [3]: The LLCM dataset can be downloaded from this website by submitting a copyright form.

• Please send a signed dataset release agreement copy to [email protected]

2. Training and Testing.

Before train the hos-net, you can download the baseline ckpt from CAJ and put it in ./baseline/ The results might be better by finetuning the hyper-parameters.

python train_hos_net.py

ckpt and log can be seen in ./sle_ckpt/, ./sle_hsl_ckpt/, and ./sle_hsl_cfl_ckpt/

3. Citation

Please kindly cite this paper in your publications if it helps your research:

@inproceedings{qiu2024high,
  title={High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification},
  author={Qiu, Liuxiang and Chen, Si and Yan, Yan and Xue, Jing-Hao and Wang, Da-Han and Zhu, Shunzhi},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={5},
  pages={4596--4604},
  year={2024}
}

4. References.

[1] A. Wu, W.-s. Zheng, H.-X. Yu, S. Gong, and J. Lai. Rgb-infrared crossmodality person re-identification. In IEEE International Conference on Computer Vision (ICCV), pages 5380–5389, 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, 17(3):605, 2017.

[3] Y. Zhang, H. Wang. Diverse embedding expansion network and low-light cross-modality benchmark for visible-infrared person re-identification. In IEEE Computer Vision and Pattern Pecognition (CVPR), pages 2153-2162, 2023.

Questions

Q1: How can we get the baseline checkpoints (e.g., CAJ-SYSU)?

A1: You can train the CAJ to get the base checkpoint (CAJ-Code).

Contact

If you have any questions, please feel free to contact us. E-mail: [email protected]