  Existing fully-supervised person re-identification (ReID) methods usually
suffer from poor generalization capability caused by domain gaps. The key to
solving this problem lies in filtering out identity-irrelevant interference and
learning domain-invariant person representations. In this paper, we aim to
design a generalizable person ReID framework which trains a model on source
domains yet is able to generalize/perform well on target domains. To achieve
this goal, we propose a simple yet effective Style Normalization and
Restitution (SNR) module. Specifically, we filter out style variations (e.g.,
illumination, color contrast) by Instance Normalization (IN). However, such a
process inevitably removes discriminative information. We propose to distill
identity-relevant feature from the removed information and restitute it to the
network to ensure high discrimination. For better disentanglement, we enforce a
dual causal loss constraint in SNR to encourage the separation of
identity-relevant features and identity-irrelevant features. Extensive
experiments demonstrate the strong generalization capability of our framework.
Our models empowered by the SNR modules significantly outperform the
state-of-the-art domain generalization approaches on multiple widely-used
person ReID benchmarks, and also show superiority on unsupervised domain
adaptation.
