Mengjia Xue

Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-Id With Feature Distribution Alignment

Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the noise from distribution discrepancy during network training, which may lead to feature misalignment and hinder the model performance. To address this problem,…