Attribute Distribution Modeling and Semantic-Visual Alignment for Generative Zero-shot Learning

• URL: http://arxiv.org/abs/2603.06281v2
• Date: Mon, 09 Mar 2026 02:33:29 GMT
• Title: Attribute Distribution Modeling and Semantic-Visual Alignment for Generative Zero-shot Learning
• Abstract summary: Generative zero-shot learning (ZSL) synthesizes features for unseen classes, leveraging semantic conditions to transfer knowledge from seen classes.<n>It also introduces two intrinsic challenges: (1) class-level attributes fails to capture instance-specific visual appearances due to substantial intra-class variability, thus causing the class-instance gap.<n>We propose an Attribute Distribution Modeling and Semantic-Visual Alignment (ADiVA) approach, jointly modeling attribute distributions and performing explicit semantic-visual alignment.
• Score: 32.153364858061614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative zero-shot learning (ZSL) synthesizes features for unseen classes, leveraging semantic conditions to transfer knowledge from seen classes. However, it also introduces two intrinsic challenges: (1) class-level attributes fails to capture instance-specific visual appearances due to substantial intra-class variability, thus causing the class-instance gap; (2) the substantial mismatch between semantic and visual feature distributions, manifested in inter-class correlations, gives rise to the semantic-visual domain gap. To address these challenges, we propose an Attribute Distribution Modeling and Semantic-Visual Alignment (ADiVA) approach, jointly modeling attribute distributions and performing explicit semantic-visual alignment. Specifically, our ADiVA consists of two modules: an Attribute Distribution Modeling (ADM) module that learns a transferable attribute distribution for each class and samples instance-level attributes for unseen classes, and a Visual-Guided Alignment (VGA) module that refines semantic representations to better reflect visual structures. Experiments on three widely used benchmark datasets demonstrate that ADiVA significantly outperforms state-of-the-art methods (e.g., achieving gains of 4.7% and 6.1% on AWA2 and SUN, respectively). Moreover, our approach can serve as a plugin to enhance existing generative ZSL methods.

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