Abstract

Existing unsupervised person re-identification (Re-ID) methods obtain pseudo-labels mainly by clustering to optimize the model. Most methods only use clustered instances to provide supervised information for model training without considering the possible value information of un-clustered outliers. Some methods use un-clustered outliers for training and show promising results, but their strategies for using un-clustered outliers are limited in applicability. Furthermore, unsatisfactory feature embedding and imperfect clustering cannot guarantee that instances within clusters have the same identity, resulting in generated pseudo-labels containing noise. To solve the above problems, outliers adaptation exploration (OAE) and centroids matching label refinement (CMLR) are proposed in this paper. First, OAE is designed to explore the value information of un-clustered outliers. OAE implements the nearest clustered instance neighborhood constraint and the maximum centroid distance constraint on the un-clustered outliers based on the feature distribution to update the clusters, thereby achieving a healthy balance between the number of training samples and model accuracy. Second, CMLR is proposed to alleviate the inherent noise in pseudo-labels. CMLR considers the similarity relationship between clustered instances and clusters in accordance with the cluster distribution to refine pseudo-labels, prompting features to learn from probability distributions with cluster distribution similarity relationship information. Extensive experiments demonstrate the effectiveness of the proposed method, which outperforms the state-of-the-art performance in unsupervised learning and unsupervised domain adaptation settings.

Recommended citation: Li L, Han Q, Min W*, et al. Outliers Adaptation Exploration and Centroids Matching Label Refinement for Unsupervised Person Re-identification[J]. IEEE Transactions on Multimedia, 2026
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