  Domain generalization (DG) serves as a promising solution to handle person
Re-Identification (Re-ID), which trains the model using labels from the source
domain alone, and then directly adopts the trained model to the target domain
without model updating. However, existing DG approaches are usually disturbed
by serious domain variations due to significant dataset variations.
Subsequently, DG highly relies on designing domain-invariant features, which is
however not well exploited, since most existing approaches directly mix
multiple datasets to train DG based models without considering the local
dataset similarities, i.e., examples that are very similar but from different
domains. In this paper, we present a Dual Distribution Alignment Network
(DDAN), which handles this challenge by mapping images into a domain-invariant
feature space by selectively aligning distributions of multiple source domains.
Such an alignment is conducted by dual-level constraints, i.e., the domain-wise
adversarial feature learning and the identity-wise similarity enhancement. We
evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID)
benchmark. Quantitative results demonstrate that the proposed DDAN can well
align the distributions of various source domains, and significantly
outperforms all existing domain generalization approaches.
