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arXiv · 2607.14821

Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

Xiao Wang·Bing Wang·Bin Yang·Cuiqun Chen·Xin Xu·Mang Ye
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Abstract

Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challenges such as low illumination and occlusion. To overcome these limitations, the field is rapidly evolving toward cross-modal and multi-modal paradigms. This survey presents a comprehensive overview of this transition, systematically reviewing key cross-modal tasks including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and the emerging Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. Furthermore, we examine tri-spectral and multi-modal fusion ReID, discussing how complementary information from diverse sensors enhances robustness. Beyond summarizing datasets, challenges, and methodologies, we propose a Transformer-based baseline framework for visible-infrared ReID, designed to effectively capture modality-invariant features. Finally, based on the current landscape, we outline several promising directions for future research.

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Xiao Wang, Bing Wang, Bin Yang, Cuiqun Chen, Xin Xu, Mang Ye. 2026-07-16. Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification. https://arxiv.org/abs/2607.14821

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