  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.
