[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning
This is the official PyTorch code for the QAConv method proposed in our paper [1] and the QAConv-GS with Graph Sampling proposed in our paper [2]. A Chinese blog is available in 再见,迁移学习?可解释和泛化的行人再辨识.
Fig. 1. Illustration of the proposed query-adaptive convolution (QAConv).
Fig. 2. Examples of local correspondences obtained by QAConv.
Fig. 3. QAConv network architecture in training.
Fig. 4. Illustration of the proposed temporal lifting (TLift).
Download some public datasets (e.g. Market-1501, CUHK03-NP, MSMT, RandPerson, ClonedPerson) on your own, extract them in some folder, and then run the followings.
python main.py --dataset market --testset cuhk03_np_detected[,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".
The main file is updated with the QAConv 2.1 version, that is the CVPR 2022 version with the Graph Sampler and sole triplet loss. For other earlier versions, please check Releases.
python main.py --dataset market --testset cuhk03_np_detected[,market,msmt] [--data-dir ./data] [--exp-dir ./Exp] --evaluate
Performance (%) of QAConv (QAConv 1.0) and QAConv-GS (QAConv 2.1) under direct cross-dataset evaluation without transfer learning or domain adaptation:
Training Data | Version | Training Hours | CUHK03-NP | Market-1501 | MSMT17 | |||
Rank-1 | mAP | Rank-1 | mAP | Rank-1 | mAP | |||
Market | QAConv 1.0 | 1.33 | 9.9 | 8.6 | - | - | 22.6 | 7.0 |
QAConv 2.1 | 0.25 | 19.1 | 18.1 | - | - | 45.9 | 17.2 | |
MSMT | QAConv 2.1 | 0.73 | 20.9 | 20.6 | 79.1 | 49.5 | - | - |
MSMT (all) | QAConv 1.0 | 26.90 | 25.3 | 22.6 | 72.6 | 43.1 | - | - |
QAConv 2.1 | 3.42 | 27.6 | 28.0 | 82.4 | 56.9 | - | - | |
RandPerson | QAConv 2.1 | 2.0 | 18.4 | 16.1 | 76.7 | 46.7 | 45.1 | 15.5 |
Shengcai Liao
Inception Institute of Artificial Intelligence (IIAI)
[email protected]
[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.
[2] Shengcai Liao and Ling Shao, "Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification." In CVF/IEEE Conference on Computer Vision and Pattern Recognition, 2022.
@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}
}
@article{Liao-CVPR2022-GraphSampling,
author = {Shengcai Liao and Ling Shao},
title = {{Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification}},
booktitle = {CVF/IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022}
}