Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

Abstract

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by the colors in images especially in the unsupervised setting. In this paper, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which cluster structure is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively. Extensive experiments conducted on three benchmark datasets demonstrate superior performance of our proposal.

References in corpus (6)

• Bootstrap your own latent: A new approach to self-supervised Learning
• Unsupervised Domain Adaptation by Backpropagation
• Person Re-identification: Past, Present and Future
• Exploring Simple Siamese Representation Learning
• Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
• Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification

Cited by in corpus (3)

• Learning to Purification for Unsupervised Person Re-identification
• A review of Recent Techniques for Person Re-Identification
• Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution