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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Vydané v:	Displays Ročník 83; s. 102717
Hlavní autori:	Zhang, Xiaowei, Dou, Xiao, Zhao, Xinpeng, Li, Guocong, Wang, Zekang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.07.2024
Predmet:	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
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