No escape: Towards suggestive clues guidance for cross-modality person re-identification

Criminal activities are frequently committed at night to avoid attention, which seriously challenges traditional re-identification (ReID) systems. Recently, visible–infrared person re-identification (VI-ReID) has been in the spotlight for wide applications in low-light scenes, aiming to match pedestrians across the inherent modality gap between infrared images (for night) and visible images (for daytime). Previous deep learning-based methods mainly bridge the modality gap either by cross-modality translation or learning modality-shared representation. However, the former inevitably damages the original modality information, while the latter ignores fine-grained intrinsic metric relationships between cross-spectral features. In this paper, we propose a suggestive-clues reconfiguration (SCR) framework, which includes representation learning and feature reconfiguration sub-networks. The representation learning is pursued in modality-shared domain, in which we suggest a local cross-alignment (LCA) loss to further optimize the metric between cross-modality clustering components and centers, exploring fine-grained modality-consistent representations. In the feature reconfiguration network, we decouple infrared and visible modality features and introduce reconfiguration encoder to learn identity-related suggestive clues, enhancing the controllability of cross-modality learning. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that our SCR is a new state-of-the-art method. Specifically, the Rank-1 and Rank-10 accuracy of SCR are 97.9% and about 100% on the RegDB dataset. Our interesting research highlights the role of suggestive clues in VI-ReID, and our code can be obtained at: https://github.com/ISCLab-Bistu/VI-ReID.