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Introduction

This is the official repo for the CVPR 2021 paper "MetaCam+DSCE".

[2021.5.24] We recorded a video on Zhidongxi.

Prerequisites

  • CUDA>=10.0

  • At least two 1080-Ti GPUs

  • Other necessary packages listed in requirements.txt

  • Training Data

    (Market-1501, DukeMTMC-reID and MSMT-17. You can download these datasets from Zhong's repo)

    Unzip all datasets and ensure the file structure is as follow:

    MetaCam_DSCE/data    
    │
    └───market1501 OR dukemtmc OR msmt17
         │   
         └───DukeMTMC-reID OR Market-1501-v15.09.15 OR MSMT17_V1
             │   
             └───bounding_box_train
             │   
             └───bounding_box_test
             | 
             └───query
             │   
             └───list_train.txt (only for MSMT-17)
             | 
             └───list_query.txt (only for MSMT-17)
             | 
             └───list_gallery.txt (only for MSMT-17)
             | 
             └───list_val.txt (only for MSMT-17)
    

Usage

See run.sh for details.

Acknowledgments

This repo borrows partially from MWNet (meta-learning), ECN (exemplar memory) and SpCL (faiss-based acceleration). If you find our code useful, please cite their papers.

Resources

  1. Pre-trained MMT-500 models to reproduce Tab. 3 of our paper. BaiduNetDisk, Passwd: jr1l. Google Drive.

  2. Pedestrian images used to plot Fig.3 in our paper. BaiduNetDisk, Passwd: f248. Google Drive.

    Please download 'marCam' and 'dukeCam', put them under 'MetaCam_DSCE/data', uncomment L#87-89 and L#163-168 of train_usl_knn_merge.py to visualize pedestrian features.

  3. Training logs. BaiduNetDisk, Passwd: mecq. Google Drive.

How to Cite

@inproceedings{yang2021joint,
  title={Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification},
  author={Yang Fengxiang and Zhong Zhun and Luo Zhiming and Cai Yuanzheng and Lin Yaojin and Li Shaozi and Nicu Sebe},
  booktitle={CVPR},
  pages={4855--4864},
  year={2021}
}

Contact Us

Email: [email protected]

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Code for our CVPR 2021 paper "MetaCam+DSCE"

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