  Domain generalization person re-identification (DG Re-ID) aims to directly
deploy a model trained on the source domain to the unseen target domain with
good generalization, which is a challenging problem and has practical value in
a real-world deployment. In the existing DG Re-ID methods, invariant operations
are effective in extracting domain generalization features, and Instance
Normalization (IN) or Batch Normalization (BN) is used to alleviate the bias to
unseen domains. Due to domain-specific information being used to capture
discriminability of the individual source domain, the generalized ability for
unseen domains is unsatisfactory. To address this problem, an Attention-aware
Multi-operation Strategery (AMS) for DG Re-ID is proposed to extract more
generalized features. We investigate invariant operations and construct a
multi-operation module based on IN and group whitening (GW) to extract
domain-invariant feature representations. Furthermore, we analyze different
domain-invariant characteristics, and apply spatial attention to the IN
operation and channel attention to the GW operation to enhance the
domain-invariant features. The proposed AMS module can be used as a
plug-and-play module to incorporate into existing network architectures.
Extensive experimental results show that AMS can effectively enhance the
model's generalization ability to unseen domains and significantly improves the
recognition performance in DG Re-ID on three protocols with ten datasets.
