Domain generalization person re-identification (DG-ReID) aims to train a
model on source domains and generalize well on unseen domains. Vision
Transformer usually yields better generalization ability than common CNN
networks under distribution shifts. However, Transformer-based ReID models
inevitably over-fit to domain-specific biases due to the supervised learning
strategy on the source domain. We observe that while the global images of
different IDs should have different features, their similar local parts (e.g.,
black backpack) are not bounded by this constraint. Motivated by this, we
propose a pure Transformer model (termed Part-aware Transformer) for DG-ReID by
designing a proxy task, named Cross-ID Similarity Learning (CSL), to mine local
visual information shared by different IDs. This proxy task allows the model to
learn generic features because it only cares about the visual similarity of the
parts regardless of the ID labels, thus alleviating the side effect of
domain-specific biases. Based on the local similarity obtained in CSL, a
Part-guided Self-Distillation (PSD) is proposed to further improve the
generalization of global features. Our method achieves state-of-the-art
performance under most DG ReID settings. Under the Market$\to$Duke setting, our
method exceeds state-of-the-art by 10.9% and 12.8% in Rank1 and mAP,
respectively. The code is available at
this https URL

我们提出了一种适用于域泛化人物重识别任务的纯 Transformer 模型，通过设计了一个名为 Cross-ID Similarity Learning (CSL) 的代理任务来挖掘不同 ID 之间共享的局部视觉信息，从而缓解领域特定偏差的副作用，并在此基础上提出了 Part-guided Self-Distillation (PSD) 来进一步提高全局特征的泛化性能。在大多数域泛化人物重识别设置下，我们的方法实现了当前最先进的性能，在 Market$	o$Duke 设置下，我们的方法在 Rank1 和 mAP 上超过了当前最先进的性能，分别提升了 10.9% 和 12.8%。