  Recently, unsupervised person re-identification (Re-ID) has received
increasing research attention due to its potential for label-free applications.
A promising way to address unsupervised Re-ID is clustering-based, which
generates pseudo labels by clustering and uses the pseudo labels to train a
Re-ID model iteratively. However, most clustering-based methods take each
cluster as a pseudo identity class, neglecting the intra-cluster variance
mainly caused by the change of cameras. To address this issue, we propose to
split each single cluster into multiple proxies according to camera views. The
camera-aware proxies explicitly capture local structures within clusters, by
which the intra-ID variance and inter-ID similarity can be better tackled.
Assisted with the camera-aware proxies, we design two proxy-level contrastive
learning losses that are, respectively, based on offline and online association
results. The offline association directly associates proxies according to the
clustering and splitting results, while the online strategy dynamically
associates proxies in terms of up-to-date features to reduce the noise caused
by the delayed update of pseudo labels. The combination of two losses enables
us to train a desirable Re-ID model. Extensive experiments on three person
Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach
demonstrates competitive performance with state-of-the-art methods. Code will
be available at: https://github.com/Terminator8758/O2CAP.
