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.

翻译：在行人重识别任务中，由于数据有限，通过深度学习学习判别性表示仍具挑战性。通常，增加数据量可提升模型性能。增加相似类别可增强分类器区分相似身份的能力，从而提高表示的判别性。本文提出一种多样性与紧凑性Transformer（DC-Former），通过将嵌入空间划分为多个多样且紧凑的子空间来实现类似效果。紧凑嵌入子空间有助于模型学习更鲁棒、更具判别性的嵌入以区分相似类别，而融合包含更细粒度信息的多样化嵌入可进一步提升重识别效果。具体而言，我们利用视觉Transformer中的多个类令牌表示多个嵌入空间，随后对其施加自多样性约束（SDC），促使各空间相互远离，从而确保每个嵌入空间的多样性与紧凑性。此外，我们进一步设计动态权重控制器（DWC），用于在训练过程中平衡各空间的相对重要性。实验结果表明，本方法在多个常用行人重识别基准上超越现有最先进方法，取得了优异性能。