Related papers: Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification

Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification

URL: http://arxiv.org/abs/2304.12634v1
Date: Tue, 25 Apr 2023 08:04:12 GMT
Title: Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification
Authors: Pengna Li, Kangyi Wu, Sanping Zhou.Qianxin Huang, Jinjun Wang
Abstract summary: Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras. We propose a novel label refinement framework with clustering intra-camera similarity.
Score: 8.779246907359706
License: http://creativecommons.org/publicdomain/zero/1.0/
Abstract: Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras, leading to inevitable performance degradation. To address this challenge, we propose a novel label refinement framework with clustering intra-camera similarity. Intra-camera feature distribution pays more attention to the appearance of pedestrians and labels are more reliable. We conduct intra-camera training to get local clusters in each camera, respectively, and refine inter-camera clusters with local results. We hence train the Re-ID model with refined reliable pseudo labels in a self-paced way. Extensive experiments demonstrate that the proposed method surpasses state-of-the-art performance.

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