Unsupervised Joint Contrastive Learning for Aerial Person Re-Identification and Remote Sensing Image Classification
Guoqing Zhang,
Jiqiang Li,
Zhonglin Ye
Abstract:Unsupervised person re-identification (Re-ID) aims to match the query image of a person with images in the gallery without the use of supervision labels. Most existing methods usually generate pseudo-labels through clustering algorithms for contrastive learning, which inevitably results in noisy labels assigned to samples. In addition, methods that only apply contrastive learning at the clustering level fail to fully consider instance-level relationships between instances. Motivated by this, we propose a joint… Show more
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