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Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

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QAConv

Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

This PyTorch code is proposed in our paper [1]. A Chinese blog is available in 再见,迁移学习?可解释和泛化的行人再辨识.

Requirements

  • Pytorch (>1.0)
  • sklearn

Usage

Download some public datasets (e.g. Market-1501, DukeMTMC-reID, CUHK03-NP, MSMT) on your own, extract them in some folder, and then run the followings.

Training and test

python main.py --dataset market --testset duke[,market,msmt] [--data-dir ./data] [--exp-dir ./Exp]

For more options, run "python main.py --help". For example, if you want to use the ResNet-152 as backbone, specify "-a resnet152". If you want to train on the whole dataset (as done in our paper for the MSMT17), specify "--combine_all".

Test only

python main.py --dataset market --testset duke[,market,msmt] [--data-dir ./data] [--exp-dir ./Exp] --evaluate

Performance

Performance (%) of QAConv with ResNet-152 under direct cross-dataset evaluation without transfer learning or domain adaptation:

Method Training set Test set Rank-1 mAP
QAConv Market Duke 54.4 33.6
QAConv + RR + TLift Market Duke 70.0 61.2
QAConv MSMT Duke 72.2 53.4
QAConv + RR + TLift MSMT Duke 82.2 78.4
QAConv Duke Market 62.8 31.6
QAConv + RR + TLift Duke Market 78.7 58.2
QAConv MSMT Market 73.9 46.6
QAConv + RR + TLift MSMT Market 88.4 76.0
QAConv Market MSMT 25.6 8.2
QAConv Duke MSMT 32.7 10.4
QAConv Market CUHK03-NP 14.1 11.8
QAConv Duke CUHK03-NP 11.0 9.4
QAConv MSMT CUHK03-NP 32.6 28.1

Pre-trained Models

The above pre-trained models can also be downloaded from Baidu (access code: 52cv), thanks to 52CV.

Contacts

Shengcai Liao
Inception Institute of Artificial Intelligence (IIAI)
shengcai.liao@inceptioniai.org

Citation

[1] Shengcai Liao and Ling Shao, "Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting." In the 16th European Conference on Computer Vision (ECCV), 23-28 August, 2020.

@inproceedings{Liao-ECCV2020-QAConv,
title={{Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting}},
author={Shengcai Liao and Ling Shao},
booktitle={European Conference on Computer Vision (ECCV)},
year={2020}
}

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Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

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  • Python 95.7%
  • MATLAB 4.3%