  In person re-identification (re-ID) task, it is still challenging to learn
discriminative representation by deep learning, due to limited data. Generally
speaking, the model will get better performance when increasing the amount of
data. The addition of similar classes strengthens the ability of the classifier
to identify similar identities, thereby improving the discrimination of
representation. In this paper, we propose a Diverse and Compact Transformer
(DC-Former) that can achieve a similar effect by splitting embedding space into
multiple diverse and compact subspaces. Compact embedding subspace helps model
learn more robust and discriminative embedding to identify similar classes. And
the fusion of these diverse embeddings containing more fine-grained information
can further improve the effect of re-ID. Specifically, multiple class tokens
are used in vision transformer to represent multiple embedding spaces. Then, a
self-diverse constraint (SDC) is applied to these spaces to push them away from
each other, which makes each embedding space diverse and compact. Further, a
dynamic weight controller(DWC) is further designed for balancing the relative
importance among them during training. The experimental results of our method
are promising, which surpass previous state-of-the-art methods on several
commonly used person re-ID benchmarks.
