Rethinking Pseudo-Label-Based Unsupervised Person Re-ID with Hierarchical Prototype-based Graph

Unsupervised person re-identification (Re-ID) aims to match individuals without manual annotations. However, existing methods often struggle with intra-class variations due to differences in person poses and camera styles such as resolution and environment information. Additionally, clustering may produce incorrect pseudo-labels, compounding the issue. To address these challenges, we propose a novel hierarchical prototype-based graph network (HPG-Net) for unsupervised person Re-ID. Our approach uses a hierarchical prototype-based graph structure to describe person images by attributes of poses and camera styles, with each graph node representing the average of image features as a prototype. We then apply a hierarchical contrastive learning module to enhance the feature learning at each level, reducing the impact of intra-class differences caused by extraneous attributes. We also calculate the similarity between samples and each level of prototypes, maintaining prototype-based graph consistency with the mean-teacher network to mitigate the accumulation errors caused by pseudo-labels. Experimental results on three benchmarks show that our method outperforms state-of-the-art (SOTA) works. Moreover, we achieve promising performance on an occluded dataset.