目标重识别技术研究综述:从单模态到多模态的演进

Survey on research of object re-identification technology: Evolution from uni-modal to multi-modal

摘要

目标重识别旨在通过提取鲁棒的特征表示,解决跨摄像头、跨场景及多模态数据下的目标匹配问题,实现同一目标在不同时间、空间和外观条件下的准确识别与跨域关联。在真实复杂应用场景中,特定目标的数据来源具有数据信息量缺乏和查询线索数据类型显著多样性等特点,针对不同模态数据源的目标重识别研究,其核心问题定义、方法论体系和技术实现路径均存在显著差异。因此,本文基于目标数据模态的视角,以单模态、跨模态和多模态为驱动的重识别研究方法为主线,对目标重识别领域的研究进展进行了全面综述。本文首先系统梳理了目标重识别的技术演进脉络,重点总结了当前主流数据集的构建、标注及适用场景,分析了数据多样性与标注完备性对模型性能的影响机制。然后重点归纳了近期研究在跨模态语义对齐及多源信息融合等关键环节的创新研究成果,涵盖基于深度表征学习、注意力机制与预训练模型的核心方法。最后,针对当前研究面临的模态差异显著、标注数据稀缺、模型泛化能力不足及计算效率受限等核心难点,结合多模态重识别领域的技术痛点与应用需求,对未来技术发展方向进行了前瞻性探讨。

Abstract

Object re-identification aims to address the object matching problem across cameras,scenarios,and multi-modal data by extracting robust feature representations,thereby achieving accurate identification and cross-domain association of the same object under different temporal,spatial,and appearance conditions.In real-world complex application scenarios,the data sources of specific targets are characterized by insufficient data information volume and significant diversity of query clue data types.For the research on object re-identification based on different modal data sources,there are prominent differences in their core problem definitions,methodological systems and technical implementation paths.Therefore,from the perspective of target data modalities,this paper conducts a comprehensive review of the research progress in the field of object re-identification,with the methods driven by uni-modal,cross-modal,and multi-modal paradigms as the main thread.First,this paper systematically outlines the technical evolution of object re-identification,focuses on summarizing the construction,annotation,and applicable scenarios of current mainstream datasets,and analyzes the mechanism by which data diversity and annotation completeness influence model performance.Then,it emphasizes the induction of innovative research achievements in key links such as cross-modal semantic alignment and multi-source information fusion in recent studies,covering core methods based on deep representation learning,attention mechanism,and pre-trained models.Finally,aiming at the core challenges faced by current research,such as significant modal differences,scarce annotated data,insufficient model generalization ability,and limited computational efficiency,this paper conducts a prospective discussion on the future direction of technological development by integrating the technical pain points and application requirements in the field of multi-modal re-identification.

关键词

目标重识别 / 深度学习 / 单模态 / 跨模态 / 多模态

Key words

object re-identification / deep learning / uni-modal / cross-modal / multi-modal

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基金资助

国家自然科学基金项目(62566013),贵州省科学计划项目(黔科合平台KXJZ[2025]022,黔科合平台人才-YQK[2023]028)