Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification

• URL: http://arxiv.org/abs/2303.06810v1
• Date: Mon, 13 Mar 2023 01:56:53 GMT
• Title: Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification
• Authors: Ziqi He, Mengjia Xue, Yunhao Du, Zhicheng Zhao, Fei Su
• Abstract summary: Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. We propose a dynamic clustering and cluster contrastive learning (DCCC) method. Experiments on several widely used public datasets validate the effectiveness of our proposed DCCC.
• Score: 29.167783500369442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the relationship between module parameters of Re-ID framework and feature distributions, which may lead to feature misalignment and hinder the model performance. To address this problem, we propose a dynamic clustering and cluster contrastive learning (DCCC) method. Specifically, we first design a dynamic clustering parameters scheduler (DCPS) which adjust the hyper-parameter of clustering to fit the variation of intra- and inter-class distances. Then, a dynamic cluster contrastive learning (DyCL) method is designed to match the cluster representation vectors' weights with the local feature association. Finally, a label smoothing soft contrastive loss ($L_{ss}$) is built to keep the balance between cluster contrastive learning and self-supervised learning with low computational consumption and high computational efficiency. Experiments on several widely used public datasets validate the effectiveness of our proposed DCCC which outperforms previous state-of-the-art methods by achieving the best performance.

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