模糊模态边界：从单模态到多模态行人重识别的统一综述
Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification
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中文总结 AI 辅助
综述行人重识别领域从单模态向多模态发展的转变，系统回顾关键跨模态任务，研究多模态融合ReID，提出基于Transformer的可见光-红外ReID基线框架，并概述未来研究方向。
中文摘要 AI 辅助
行人重识别（ReID）是智能监控系统的关键组成部分，旨在跨不相交摄像头网络匹配身份。传统方法主要依赖单模态RGB图像，常受低光照和遮挡等环境挑战限制。为克服这些局限，该领域正迅速向跨模态和多模态范式发展。本综述全面介绍这一转变，系统回顾关键跨模态任务，包括可见光-红外（VI-ReID）、文本-图像（TI-ReID）、基于草图（Sketch-ReID）以及新兴的非视距（NLOS）ReID等。还研究了三光谱和多模态融合ReID，探讨不同传感器的互补信息如何增强鲁棒性。除总结数据集、挑战和方法外，提出基于Transformer的可见光-红外ReID基线框架，以有效捕捉模态不变特征。最后基于当前情况，概述未来研究的几个有前景方向。
英文摘要
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.
发表机构
- Wuhan University of Science and Technology（武汉科技大学）
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System（湖北省智能信息处理与实时工业系统重点实验室）
- Wuhan University（武汉大学）
- Anhui University（安徽大学）
机构由 AI 辅助整理，请以论文原文为准。
