  Most of unsupervised person Re-Identification (Re-ID) works produce
pseudo-labels by measuring the feature similarity without considering the
distribution discrepancy among cameras, leading to degraded accuracy in label
computation across cameras. This paper targets to address this challenge by
studying a novel intra-inter camera similarity for pseudo-label generation. We
decompose the sample similarity computation into two stage, i.e., the
intra-camera and inter-camera computations, respectively. The intra-camera
computation directly leverages the CNN features for similarity computation
within each camera. Pseudo-labels generated on different cameras train the
re-id model in a multi-branch network. The second stage considers the
classification scores of each sample on different cameras as a new feature
vector. This new feature effectively alleviates the distribution discrepancy
among cameras and generates more reliable pseudo-labels. We hence train our
re-id model in two stages with intra-camera and inter-camera pseudo-labels,
respectively. This simple intra-inter camera similarity produces surprisingly
good performance on multiple datasets, e.g., achieves rank-1 accuracy of 89.5%
on the Market1501 dataset, outperforming the recent unsupervised works by 9+%,
and is comparable with the latest transfer learning works that leverage extra
annotations.
