Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains

• URL: http://arxiv.org/abs/2107.07497v1
• Date: Thu, 15 Jul 2021 17:51:16 GMT
• Title: Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
• Authors: Puneet Mangla, Shivam Chandhok, Vineeth N Balasubramanian and Fahad Shahbaz Khan
• Abstract summary: We propose a feature generative framework integrated with a COntext COnditional Adaptive (COCOA) Batch-Normalization. The generated visual features better capture the underlying data distribution enabling us to generalize to unseen classes and domains at test-time. We thoroughly evaluate and analyse our approach on established large-scale benchmark - DomainNet.
• Score: 48.17225008334873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i.e zero-shot domain generalization). For models to generalize to unseen classes in unseen domains, it is crucial to learn feature representation that preserves class-level (domain-invariant) as well as domain-specific information. Motivated from the success of generative zero-shot approaches, we propose a feature generative framework integrated with a COntext COnditional Adaptive (COCOA) Batch-Normalization to seamlessly integrate class-level semantic and domain-specific information. The generated visual features better capture the underlying data distribution enabling us to generalize to unseen classes and domains at test-time. We thoroughly evaluate and analyse our approach on established large-scale benchmark - DomainNet and demonstrate promising performance over baselines and state-of-art methods.

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