  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.
