Learning Domain Invariant Representations for Generalizable Person Re-Identification
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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.
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