This is the official repo for the CVPR 2021 paper "MetaCam+DSCE".
[2021.5.24] We recorded a video on Zhidongxi.
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)
See run.sh for details.
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.
Pre-trained MMT-500 models to reproduce Tab. 3 of our paper. BaiduNetDisk, Passwd: jr1l. Google Drive.
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.
Training logs. BaiduNetDisk, Passwd: mecq. Google Drive.
@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}
}
Email: yangfx@stu.xmu.edu.cn