Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification

This is the official implementation of the paper "Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification"

Getting Started

git clone https://github.com/KimbingNg/DeskPro && cd DeskPro

Download the prepared datasets and the pretrained teacher models from this link, and runs

tar -zxvf dataset.tar.gz
pip3 install -r ./requirements.txt

Train on Celeb:

dataset=celeb
CUDA_VISIBLE_DEVICES=0 python3 main.py --config ./config_files/celeb_config.yaml \
 data_test $dataset"_lr_hr_mask" data_train $dataset"_lr_hr_mask" \
 loss '1*CrossEntropy+1*Triplet' \
 tag 'exp_version' \
 batchid 5 \
 batchimage 6 \
 kd_loss.enable True \
 kd_loss.T 5. \
 kd_loss.alpha 0.7 \
 mse.mse_weight 7.0 \
 mse.spatial_attn_lr 1.0 \
 forward_mode all \
 pre_train "$dataset"_teacher.pth

Train on Celeb-light:

dataset=celeb-light
CUDA_VISIBLE_DEVICES=0 python3 main.py --config ./config_files/celeb_config.yaml \
 data_test $dataset"_lr_hr_mask" data_train $dataset"_lr_hr_mask" \
 loss '1*CrossEntropy+1*Triplet' \
 tag 'exp_version' \
 batchid 5 \
 batchimage 6 \
 kd_loss.enable True \
 kd_loss.T 5. \
 kd_loss.alpha 0.7 \
 mse.mse_weight 7.0 \
 mse.spatial_attn_lr 1.0 \
 forward_mode all \
 pre_train "$dataset"_teacher.pth

Train on PRCC:

dataset=prcc
CUDA_VISIBLE_DEVICES=0 python3 main.py --config ./config_files/prcc_config.yaml \
 data_test $dataset"_lr_hr_mask" data_train $dataset"_lr_hr_mask" \
 loss '1*CrossEntropy+1*Triplet' \
 tag 'exp_version' \
 batchid 5 \
 batchimage 6 \
 kd_loss.enable True \
 kd_loss.T 1. \
 kd_loss.alpha 0.8 \
 mse.mse_weight 7.0 \
 mse.spatial_attn_lr 1.0 \
 forward_mode all \
 pre_train "$dataset"_teacher.pth

Citation:

If you find this work useful in your research, please consider citing:

@inproceedings{wuIdentitySensitiveKnowledgePropagation2022,
  title = {Identity-{{Sensitive Knowledge Propagation}} for {{Cloth-Changing Person Re-identification}}},
  booktitle = {2022 {{IEEE International Conference}} on {{Image Processing}}},
  author = {Wu, Jianbing and Liu, Hong and Shi, Wei and Tang, Hao and Guo, Jingwen},
  year = {2022},
  publisher = {{IEEE}}
}

Acknowledgments

The codes was built on top of deep-person-reid, reid-strong-baseline, MGN-pytorch, and LightMBN, we thank the authors for sharing their code publicly.