  Generalizable person Re-Identification (ReID) has attracted growing attention
in recent computer vision community, as it offers ready-to-use ReID models
without the need for model retraining in new environments. In this work, we
introduce causality into person ReID and propose a novel generalizable
framework, named Domain Invariant Representations for generalizable person
Re-Identification (DIR-ReID). We assume the data generation process is
controlled by two sets of factors, i.e. identity-specific factors containing
identity related cues, and domain-specific factors describing other
scene-related information which cause distribution shifts across domains. With
the assumption above, a novel Multi-Domain Disentangled Adversarial Network
(MDDAN) is designed to disentangle these two sets of factors. Furthermore, a
Causal Data Augmentation (CDA) block is proposed to perform feature-level data
augmentation for better domain-invariant representations, which can be
explained as interventions on latent factors from a causal learning
perspective. Extensive experiments have been conducted, showing that DIR-ReID
outperforms state-of-the-art methods on large-scale domain generalization (DG)
ReID benchmarks. Moreover, a theoretical analysis is provided for a better
understanding of our method.
