Multi-Scale Explicit Matching and Mutual Subject Teacher Learning for Generalizable Person Re-Identification

• Published in: IEEE transactions on circuits and systems for video technology Vol. 34; no. 9; pp. 8881 - 8895
• Main Authors: Chen, Kaixiang, Fang, Pengfei, Ye, Zi, Zhang, Liyan
• Format: Journal Article
• Language: English
• Published: IEEE 01.09.2024
• Subjects: Data models; domain generalization; Feature extraction; Identification of persons; multi-scale; mutual-teacher; Pedestrians; Person re-identification; Protocols; Task analysis; Training
• DOI: 10.1109/TCSVT.2024.3382322
• ISSN: 1051-8215

Domain generalization in person re-identification (DG-ReID) stands out as the most challenging task and practically important branch in the ReID field, which enables the direct deployment of pre-trained models in unseen and real scenarios. Recent works have made significant efforts in this task via the image-matching paradigm, which searches for the local correspondences in the feature maps. A common practice of employing pixel-wise matching is typically used to ensure efficient matching. This, however, makes the matching susceptible to deviations caused by identity-irrelevant pixel features. On the other hand, patch-wise matching also demonstrates that it will disregard the spatial orientation of pedestrians and amplify the impact of noise. To address the mentioned issues, this paper proposes the Multi-Scale Query-Adaptive Convolution (QAConv-MS) framework, which encodes patches in the feature maps to pixels using template kernels of various scales. This enables the matching process to enjoy broader receptive fields and robustness to orientations and noises. To stabilize the matching process and facilitate the independent learning of each sub-kernel within the template kernels to capture diverse local patterns, we propose the OrthoGonal Norm (OGNorm), which consists of two orthogonal normalizations. We also present Mutual Subject Teacher Learning (MSTL) to address the potential issues of overconfidence and overfitting in the model. MSTL allows two models to individually select the most challenging data for training, resulting in more dependable soft labels that can provide mutual supervision. Extensive experiments conducted in both single-source and multi-source setups offer compelling evidence of our framework's generalization and competitiveness.