  Recent years have witnessed significant progress in person re-identification
(ReID). However, current ReID approaches still suffer from considerable
performance degradation when unseen testing domains exhibit different
characteristics from the source training ones, known as the domain
generalization problem. Given multiple source training domains, previous Domain
Generalizable ReID (DG-ReID) methods usually learn all domains together using a
shared network, which can't learn sufficient knowledge from each domain. In
this paper, we propose a novel Multiple Domain Experts Collaborative Learning
(MECL) framework for better exploiting all training domains, which benefits
from the proposed Domain-Domain Collaborative Learning (DDCL) and
Universal-Domain Collaborative Learning (UDCL). DDCL utilizes domain-specific
experts for fully exploiting each domain, and prevents experts from
over-fitting the corresponding domain using a meta-learning strategy. In UDCL,
a universal expert supervises the learning of domain experts and continuously
gathers knowledge from all domain experts. Note, only the universal expert will
be used for inference. Extensive experiments on DG-ReID benchmarks demonstrate
the effectiveness of DDCL and UDCL, and show that the whole MECL framework
significantly outperforms state-of-the-arts. Experimental results on
DG-classification benchmarks also reveal the great potential of applying MECL
to other DG tasks.
