  Domain generalizable (DG) person re-identification (ReID) aims to test across
unseen domains without access to the target domain data at training time, which
is a realistic but challenging problem. In contrast to methods assuming an
identical model for different domains, Mixture of Experts (MoE) exploits
multiple domain-specific networks for leveraging complementary information
between domains, obtaining impressive results. However, prior MoE-based DG ReID
methods suffer from a large model size with the increase of the number of
source domains, and most of them overlook the exploitation of domain-invariant
characteristics. To handle the two issues above, this paper presents a new
approach called Mimic Embedding via adapTive Aggregation (META) for DG person
ReID. To avoid the large model size, experts in META do not adopt a branch
network for each source domain but share all the parameters except for the
batch normalization layers. Besides multiple experts, META leverages Instance
Normalization (IN) and introduces it into a global branch to pursue invariant
features across domains. Meanwhile, META considers the relevance of an unseen
target sample and source domains via normalization statistics and develops an
aggregation module to adaptively integrate multiple experts for mimicking
unseen target domain. Benefiting from a proposed consistency loss and an
episodic training algorithm, META is expected to mimic embedding for a truly
unseen target domain. Extensive experiments verify that META surpasses
state-of-the-art DG person ReID methods by a large margin. Our code is
available at https://github.com/xbq1994/META.
