The official repository for Multi-Modal Object Re-Identification via Sparse Mixture-of-Experts [pdf]

mkdir data

Download the person datasets RGBNT201, RGBNT100 (code：rjin), and the MSVR310.

pip install -r requirements.txt

You need to download the pretrained CLIP model: ViT-B-16 (Code：52fu)

You can train the MFRNet with:

python train_net.py --config_file configs/RGBNT201/MFRNet.yml

Some examples:

python train_net.py --config_file configs/RGBNT201/MFRNet.yml

1. The device ID to be used can be set in config/defaults.py

2. If you need to train on the RGBNT100 and MSVR310 datasets, please ensure the corresponding path is modified accordingly.

python test_net.py --config_file 'choose which config to test' --model_path 'your path of trained checkpoints'

Some examples:

python test_net.py --config_file configs/MSVR310/MFRNet.yml --model_path MSVR310_MFRNetbest.pth

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

@inproceedings{fengmulti,
  title={Multi-Modal Object Re-identification via Sparse Mixture-of-Experts},
  author={Feng, Yingying and Li, Jie and Xie, Chi and Tan, Lei and Ji, Jiayi},
  booktitle={Forty-second International Conference on Machine Learning}
}

Our code is based on TOP-ReID[1]

[1]Wang Yuhao, Liu Xuehu, Zhang Pingping, Lu Hu, Tu Zhengzheng, Lu Huchuan. 2024. TOP-ReID: Multi-Spectral Object Re-identification with Token Permutation. Proceedings of the AAAI Conference on Artificial Intelligence. 38. 5758-5766.

If you have any question, please feel free to contact us. E-mail: lei.tan@nus.edu.sg