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Dual-Semantic Consistency Learning for Visible-Infrared Person Re-Identification (TIFS 2022)


Paper PDF

This repository is an official implementation of DSCNet, a strong baseline for VI-ReID

📈 News

2022.10.24 DSCNet has been formally accepted by IEEE Transactions on Information Forensics & Security.
2022.10.15 Code Release.

🚀 Highlight

  1. Insights: This paper derives the modality discrepancy from the channel-level semantic inconsistency. It is the FIRST method to address the limitations on the channel-level representation.
  2. A strong baseline: Faster Convergence and Outstanding Performance for VI-ReID.
Model Training Epochs Rank-1 (%) mAP(%) Training Time
MCLNet 200 65.30 61.59 24 hours
DSCNet 50 73.89 69.47 5 hours

⚙️ Setup environment

  • Clone this repo:
git clone https://github.com/bitreidgroup/DSCNet.git && cd DSCNet
  • Create a conda environment and activate the environment.
conda env create -f environment.yaml &&  conda activate dsc

We recommend Python = 3.6, CUDA = 10.0, Cudnn = 7.6.5, Pytorch = 1.2, and CudaToolkit = 10.0.130 for the environment.

🔧 Preparing dataset

  • SYSU-MM01 Dataset : The SYSU-MM01 dataset can be downloaded from this website.

  • We preprocess the SYSU-MM01 dataset to speed up the training process. The identities of cameras will be also stored in ".npy" format.

python utils/pre_process_sysu.py
  • RegDB Dataset : 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).

⏳ Training

You may need manually define the data path first. More details are in the config files.

  • SYSU-MM01 Dataset (all-search)
bash scripts/train_sysu_all.sh
  • SYSU-MM01 Dataset (indoor-search)
bash scripts/train_sysu_indoor.sh

💽 Testing

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH python scripts/test.py --ckpt [CKPT_PATH] --config [CONFIT] 

For example : You can test the checkpoints by running the commands below.

bash scripts/eval_sysu.sh

⏰ Reproduce our experimental results

DSCNet: We provide some experimental results on the SYSU-MM01 datasets with pretrained models. These model are trained on 1x 2080ti

config Rank-1(%) Rank-10(%) mAP(%) Training Log Pretrained
SYSU-MM01(all-search) 73.89 96.27 69.47 log(TBA) Weights
SYSU-MM01(indoor-search) 79.35 95.74 82.68 log(TBA) Weights(TBA)

Before running the commands below, please update the config files on the setting of resume.

python scripts/reproduce.sh

💾GPUs

All our experiments were performed on a single NVIDIA GeForce 2080 Ti GPU

Training Datasets Approximate GPU memory Approximate training time
SYSU-MM01 9GB 5 hours
RegDB 6GB 3 hours

Citation

If this repository helps your research, please cite :

@article{zhang2022dual,
  title={Dual-Semantic Consistency Learning for Visible-Infrared Person Re-Identification},
  author={Zhang, Yiyuan and Kang, Yuhao and Zhao, Sanyuan and Shen, Jianbing},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2022},
  publisher={IEEE}
}

📄 References.

  1. Y. Zhang, Y. Kang, S. Zhao, and J. Shen. Dual-Semantic Consistency Learning for Visible-Infrared Person Re-Identification. IEEE Transactions on Information Forensics & Security, 2022.
  2. M. Ye, W. Ruan, B. Du, and M. Shou. Channel Augmented Joint Learning for Visible-Infrared Recognition. IEEE International Conference on Computer Vision (ICCV), 2021.

✉️ Contact.

If you have some questions, feel free to contact me. [email protected]