南京邮电大学学报（自然科学版）2024，Vol.44Issue(6)：65-75,11.DOI:10.14132/j.cnki.1673-5439.2024.06.007
基于域对齐和伪标签细化的域自适应行人重识别算法
Domain adaptive person re-identification via domain alignment and mutual pseudo label refinement
摘要
Abstract
Unsupervised domain adaptive person re-identification refers to transferring knowledge from the labeled dataset to the unlabeled,the need for large amounts of labeled data can be alleviated.The existing methods that address this problem usually use clustering methods to generate pseudo labels.However,those pseudo labels can be unstable and noisy,and can significantly degrade the performance of the methods.In this paper,we propose a novel domain adaptive person re-identification method via domain alignment and mutual pseudo label refinement.Firstly,we extract discriminative feature from the augmented data using a two-branch structure to enrich the feature diversity.Secondly,we design a distributed adversarial domain alignment module to minimize domain differences.Finally,thanks to the complementary relationship between the local and the global features,we establish the consistency between the two kinds of features to refine pseudo labels predicted by the global features,and thus the noise generated by pseudo label clustering is effectively reduced.Extensive experiments demonstrate that the proposed method can achieve remarkable results on popular benchmark datasets for domain adaptive person re-identification.关键词
行人重识别/域自适应/域对齐/伪标签细化Key words
person re-identification/domain adaptation/domain alignment/pseudo label refinement分类
信息技术与安全科学引用本文复制引用
朱松豪,宋杰..基于域对齐和伪标签细化的域自适应行人重识别算法[J].南京邮电大学学报（自然科学版）,2024,44(6):65-75,11.基金项目
国家自然科学基金(62001247)资助项目 （62001247）
