  Supervised person re-identification (ReID) often has poor scalability and
usability in real-world deployments due to domain gaps and the lack of
annotations for the target domain data. Unsupervised person ReID through domain
adaptation is attractive yet challenging. Existing unsupervised ReID approaches
often fail in correctly identifying the positive samples and negative samples
through the distance-based matching/ranking. The two distributions of distances
for positive sample pairs (Pos-distr) and negative sample pairs (Neg-distr) are
often not well separated, having large overlap. To address this problem, we
introduce a global distance-distributions separation (GDS) constraint over the
two distributions to encourage the clear separation of positive and negative
samples from a global view. We model the two global distance distributions as
Gaussian distributions and push apart the two distributions while encouraging
their sharpness in the unsupervised training process. Particularly, to model
the distributions from a global view and facilitate the timely updating of the
distributions and the GDS related losses, we leverage a momentum update
mechanism for building and maintaining the distribution parameters (mean and
variance) and calculate the loss on the fly during the training.
Distribution-based hard mining is proposed to further promote the separation of
the two distributions. We validate the effectiveness of the GDS constraint in
unsupervised ReID networks. Extensive experiments on multiple ReID benchmark
datasets show our method leads to significant improvement over the baselines
and achieves the state-of-the-art performance.
