  Unsupervised person re-identification aims to retrieve images of a specified
person without identity labels. Many recent unsupervised Re-ID approaches adopt
clustering-based methods to measure cross-camera feature similarity to roughly
divide images into clusters. They ignore the feature distribution discrepancy
induced by camera domain gap, resulting in the unavoidable performance
degradation. Camera information is usually available, and the feature
distribution in the single camera usually focuses more on the appearance of the
individual and has less intra-identity variance. Inspired by the observation,
we introduce a \textbf{C}amera-\textbf{A}ware \textbf{L}abel
\textbf{R}efinement~(CALR) framework that reduces camera discrepancy by
clustering intra-camera similarity. Specifically, we employ intra-camera
training to obtain reliable local pseudo labels within each camera, and then
refine global labels generated by inter-camera clustering and train the
discriminative model using more reliable global pseudo labels in a self-paced
manner. Meanwhile, we develop a camera-alignment module to align feature
distributions under different cameras, which could help deal with the camera
variance further. Extensive experiments validate the superiority of our
proposed method over state-of-the-art approaches. The code is accessible at
https://github.com/leeBooMla/CALR.
