  Unsupervised person re-identification (re-ID) remains a challenging task.
While extensive research has focused on the framework design and loss function,
this paper shows that sampling strategy plays an equally important role. We
analyze the reasons for the performance differences between various sampling
strategies under the same framework and loss function. We suggest that
deteriorated over-fitting is an important factor causing poor performance, and
enhancing statistical stability can rectify this problem. Inspired by that, a
simple yet effective approach is proposed, termed group sampling, which gathers
samples from the same class into groups. The model is thereby trained using
normalized group samples, which helps alleviate the negative impact of
individual samples. Group sampling updates the pipeline of pseudo-label
generation by guaranteeing that samples are more efficiently classified into
the correct classes. It regulates the representation learning process,
enhancing statistical stability for feature representation in a progressive
fashion. Extensive experiments on Market-1501, DukeMTMC-reID and MSMT17 show
that group sampling achieves performance comparable to state-of-the-art methods
and outperforms the current techniques under purely camera-agnostic settings.
Code has been available at https://github.com/ucas-vg/GroupSampling.
