Meta Distribution Alignment for Generalizable Person Re-Identification

Hao Ni1; Jingkuan Song1; Xiaopeng Luo1; Feng Zheng2; Wen Li1; Heng Tao Shen1

10.1109/CVPR52688.2022.00252

2022

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

1063-6919

978-1-6654-6947-0

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

2477-2486

18-24 June 2022

New Orleans, LA, USA

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

Domain Generalizable (DG) person ReID is a challenging task which trains a model on source domains yet generalizes well on target domains. Existing methods use source domains to learn domain-invariant features, and assume those features are also irrelevant with target domains. However, they do not consider the target domain information which is unavailable in the training phrase of DG. To address this issue, we propose a novel Meta Distribution Alignment (MDA) method to enable them to share similar distribution in a test-time-training fashion. Specifically, since high-dimensional features are difficult to constrain with a known simple distribution, we first introduce an intermediate latent space constrained to a known prior distribution. The source domain data is mapped to this latent space and then reconstructed back. A meta-learning strategy is introduced to facilitate generalization and support fast adaption. To reduce their discrepancy, we further propose a test-time adaptive updating strategy based on the latent space which efficiently adapts model to unseen domains with a few samples. Extensive experimental results show that our model outperforms the state-of-the-art methods by up to 5.2% R-1 on average on the large-scale and 4.7% R-1 on the single-source domain generalization ReID benchmark. Source code is publicly available at https://github.com/haoni0812/MDA.git.

Vision applications and systems retrieval Recognition: detection categorization Self-& semi-& meta- Transfer/low-shot/long-tail learning

其他

英语

CPCI-S

National Key Research and Development Program of China[2018AAA0102200]

Computer Science ; Imaging Science & Photographic Technology

Computer Science, Artificial Intelligence ; Imaging Science & Photographic Technology

WOS:000867754202073

IEEE

1.School of Computer Science and Engineering, University of Electronic Science and Technology of China
2.Southern University of Science and Technology

Hao Ni,Jingkuan Song,Xiaopeng Luo,et al. Meta Distribution Alignment for Generalizable Person Re-Identification[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2022:2477-2486.