Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, termed MixNorm, which consists of domain-aware mix-normalization (DMN) and domain-ware center regularization (DCR). Different from the conventional data augmentation, the proposed domain-aware mix-normalization to enhance the diversity of features during training from the normalization view of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain. To better learn the domain-invariant model, we further develop the domain-aware center regularization to better map the produced diverse features into the same space. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.

本文提出了一种新方法MixNorm来从数据增强的角度解决多源人物再识别任务的问题，包括domain-aware mix-normalization来增强模型的泛化能力并解决过拟合问题以及domain-aware center regularization来更好地学习domain-invariant模型。实验结果表明该方法可以优于现有的方法，并具备优越性。

一种新的混合归一化方法对多源人员可泛化重新识别