少样本行人重识别研究综述

doi:10.3778/j.issn.1002-8331.2410-0267

摘要/Abstract

摘要： 行人重识别任务通常依赖于大规模标注数据。然而，在少样本场景中，标注数据的有限性导致模型难以充分学习到类别间的判别性特征。为应对这一挑战，研究者们提出了多种方法以提升模型在数据匮乏条件下的性能表现。根据所需标注数据的依赖程度，相关研究可以归纳为以下四个主要类别：有监督学习的方法、基于弱监督学习的方法、基于半监督学习的方法、基于无监督学习的方法。随着对标注数据依赖的逐渐降低，行人重识别领域呈现出从有监督学习到无监督学习的技术发展趋势。系统性地总结了当前在数据集和评估指标上的研究现状，并对未来可能的研究方向进行了展望。

关键词: 行人重识别（ReID）, 少样本学习, 目标检测, 图像识别

Abstract: The task of person re-identification (ReID) typically relies on large-scale annotated datasets. However, in few-shot scenarios, the limited availability of annotated data makes it challenging for models to learn discriminative features across categories effectively. To address this challenge, researchers have proposed various methods to enhance model performance under data-scarce conditions. Based on the dependency on annotated data, related studies can be categorized into four main approaches： supervised learning, weakly supervised learning, semi-supervised learning, and unsupervised learning. As the reliance on annotated data decreases, the ReID field demonstrates a technical progression from supervised learning to unsupervised learning. This paper systematically summarizes the current research status in terms of datasets and evaluation metrics, and provides insights into potential future research directions.

Key words: person re-identification(ReID), few shot learning, object detection, image recognition

闫铭, 李雷孝, 林浩, 史建平, 平灿. 少样本行人重识别研究综述[J]. 计算机工程与应用, 2025, 61(17): 62-88.

YAN Ming, LI Leixiao, LIN Hao, SHI Jianping, PING Can. Survey of Research on Few-Shot Person Re-Identification[J]. Computer Engineering and Applications, 2025, 61(17): 62-88.

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