基于伪标签的无监督领域自适应行人重识别研究综述

Review of Unsupervised Domain Adaptive Person Re-identification Based on Pseudo-labels

行人重识别是计算机视觉领域的热点研究课题之一。近年来,为了解决行人重识别实际应用中标签数据稀缺的问题,同时也为了有效地利用现有的标签数据,研究者们提出了基于生成对抗网络以及基于伪标签的领域自适应方法,用于进行跨领域的行人重识别研究。基于伪标签的无监督领域自适应行人重识别方法由于效果显著而备受研究者的青睐。文中梳理了近7年来基于伪标签的无监督领域自适应行人重识别的研究成果,将基于伪标签的方法从模型训练角度划分为两个阶段。1)伪标签生成阶段。现有工作的伪标签生成方法大多使用聚类方法,部分工作采用基于图结构学习的图匹配、图卷积网络方法来生成目标域的伪标签。2)伪标签精炼阶段。文中将现有的伪标签精炼方法归纳为基于表征学习的精炼方法以及基于相似度学习的精炼方法,并分别进行模型方法的总结与整理。最后,讨论现阶段基于伪标签的无监督领域自适应行人重识别面临的挑战并对未来可能的发展方向进行展望。

Person re-identification is one of the hot research topics in the field of computer vision.In recent years,in order to solve the problem of scarcity of label data in the practical application of person re-identification,and to effectively use the existing label data,researchers have proposed domain adaptive methods based on generative adversarial networks and pseudo-labels to carry out cross-domain person re-identification research.The unsupervised domain adaptive person re-identification method based on pseudo-labels is favored by researchers due to its remarkable effect.This paper sorts out the work of pseudo-label-based adaptive person re-identification in the unsupervised field in the past 7 years,and divides the pseudo-label-based method into two stages from the perspective of model training:1)Pseudo-label generation stage.Most of the pseudo-label generation methods in existing works use clustering methods,and some works use graph matching based on graph structure learning and graph neural network methods to generate pseudo-labels in the target domain.2)Pseudo-label refining stage.In this paper,the existing pseudo-label refinement methods are summarized into the refinement method based on representation learning and the refinement method based on similarity learning,and the model methods are summarized and organized respectively.Finally,the current challenges of pseudo-label-based adaptive person re-identification in the unsupervised domain are discussed and the possible future development directions are prospected.