GitHub - FlyingRoastDuck/MetaCam_DSCE: Code for our CVPR 2021 paper "MetaCam+DSCE" (original) (raw)

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification (CVPR'21)

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) andSpCL (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: yangfx@stu.xmu.edu.cn