从单模到多模，统一梳理行人重识别技术演进
Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification
- 系统梳理可见光-红外、文本-图像等跨模态重识别方法
- 提出基于Transformer的可见光-红外重识别基线框架
- 适合关注智能监控与多模态融合的科研人员阅读
行人重识别（ReID）是智能监控系统的关键组件，旨在跨不同摄像头网络匹配同一身份。传统方法主要依赖单模态可见光图像，易受光照不足和遮挡影响。为克服这些局限，领域正快速向跨模态与多模态范式发展。本综述系统回顾了可见光-红外（VI-ReID）、文本-图像（TI-ReID）、草图-图像（Sketch-ReID）以及新兴的非视距（NLOS）ReID等关键跨模态任务。同时探讨三波段与多模态融合ReID，分析异构传感器互补信息如何提升鲁棒性。除总结数据集、挑战与方法外，还提出一个基于Transformer的可见光-红外重识别基线框架，以有效捕捉模态不变特征。最后，基于当前研究格局，展望未来若干有前景的方向。
原文摘要 · Abstract (English)
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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