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Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification (CVPR 2021)

<!-- ### Introduction This is the Pytorch implementation for M<sup>3</sup>L. -->

Requirements

<!-- For CUHK03 dataset, we use the old protocol (CUHK03) as the source domain for training the model and the detected subset of the new protocol (CUHK-NP) as the target domain for evaluation. For MSMT17, we use the MSMT17_V2 for both training and testing. We recommend using *the detected subset of CUHK-NP* and *MSMT17_V1* for both training and testing and we will add the results with them at a later date. --> </b>

Run

ARCH=resMeta/IBNMeta
SRC1/SRC2/SRC3=market1501/dukemtmc/cuhk03/cuhknp/msmt17v1/msmt17v2
TARGET=market1501/dukemtmc/cuhknp/msmt17v1/msmt17v2

# train
CUDA_VISIBLE_DEVICES=0,1,2 python main.py \
-a $ARCH --BNNeck \
--dataset_src1 $SRC1 --dataset_src2 $SRC2 --dataset_src3 $SRC3 -d $TARGET \
--logs-dir $LOG_DIR --data-dir $DATA_DIR

# evaluate
python main.py \
-a $ARCH -d $TARGET \
--logs-dir $LOG_DIR --data-dir $DATA_DIR \
--evaluate --resume $RESUME

Results

You can download the above models in the paper from Google Drive. The model is named as $TARGET_$ARCH.pth.tar.

Acknowledgments

This repo borrows partially from MWNet, ECN and SpCL.

Citation

@inproceedings{zhao2021learning,
  title={Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification},
  author={Zhao, Yuyang and Zhong, Zhun and Yang, Fengxiang and Luo, Zhiming and Lin, Yaojin and Li, Shaozi and Nicu, Sebe},
  booktitle={CVPR},
  year={2021},
}

Contact

Email: yuyangzhao98@gmail.com