Chaitra Jambigi
ABSTRACT
Learning identity-aware, domain-invariant representations is crucial in solving domain generalizable person ReID (DG-ReID). Existing methods commonly use augmentation techniques either in feature space by mixing instance and batch normalization layers or in pixel space by adversarially generating pseudo domains. However, neither of these techniques guarantee identity preservation. Apart from increasing training data diversity, the augmented positive pairs also encode rich semantic relations which have not been fully explored. To address the above issues, we propose a novel framework for Generalizable Person Re-identification using Domain Invariant Contrastive Techniques (G-PReDICT). Specifically, we use simple yet effective perturbation strategies to hallucinate positive samples across domains by realistically modelling domain variations while preserving the target identities. We harness rich sample-sample relations between the hallucinated positive-negative pairs to learn domain-invariant representations using supervised contrastive learning. We also use a domain independent auxiliary task, i.e. attribute prediction to learn robust representations and introduce attribute annotations for two large scale public benchmarks i.e. CUHK-03 and MSMT17. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method.
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Index Terms
- G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniques✱
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