  Generalizable person Re-Identification (ReID) has attracted growing attention
in recent computer vision community. In this work, we construct a structural
causal model among identity labels, identity-specific factors (clothes/shoes
color etc), and domain-specific factors (background, viewpoints etc). According
to the causal analysis, we propose a novel Domain Invariant Representation
Learning for generalizable person Re-Identification (DIR-ReID) framework.
Specifically, we first propose to disentangle the identity-specific and
domain-specific feature spaces, based on which we propose an effective
algorithmic implementation for backdoor adjustment, essentially serving as a
causal intervention towards the SCM. Extensive experiments have been conducted,
showing that DIR-ReID outperforms state-of-the-art methods on large-scale
domain generalization ReID benchmarks.
