Instance-aware diversity feature generation for unsupervised person re-identification

Unsupervised person re-identification (Re-ID) methods have made significant progress by exploiting contrastive learning from unlabeled data. However, the previous approaches including cluster-level or instance-level contrast loss, did not fully explore inherent commonality of each identified individ...

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Veröffentlicht in:	Displays Jg. 83; S. 102717
Hauptverfasser:	Zhang, Xiaowei, Dou, Xiao, Zhao, Xinpeng, Li, Guocong, Wang, Zekang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.07.2024
Schlagworte:	Contrastive learning Diversity features Masked autoencoder Pseudo label Unsupervised re-ID Masked autoencoder Diversity features Unsupervised re-ID Pseudo label Contrastive learning
ISSN:	0141-9382, 1872-7387
Online-Zugang:	Volltext
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