Variational Adapter for Cross-modal Similarity Representation

• URL: http://arxiv.org/abs/2605.30968v1
• Date: Fri, 29 May 2026 08:08:15 GMT
• Title: Variational Adapter for Cross-modal Similarity Representation
• Abstract summary: We propose a Variational Adapter for Cross-modal Similarity Representation (VACSR)<n>It reformulates image-text matching with fine-grained semantic scarcity as a variational inference problem.<n>It constructs a latent space for cross-modal similarity and uses regularization techniques to mitigate overfitting to binary annotations.
• Score: 2.0621012870194755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The core of vision-language models lies in measuring cross-modal similarity within a unified representation space. However, most image-text matching or multi-class image classification datasets lack fine-grained cross-modal matching annotations, forcing the continuous similarity space into binary classification boundaries. This compression induces false negative samples and significantly impairs the generalization performance of cross-modal tasks. While prior research has attempted to mitigate this by modeling intra-modal ambiguity, it often overlooks inherent annotation flaws, leading to suboptimal uncertainty allocation. To address these challenges, we propose a Variational Adapter for Cross-modal Similarity Representation (VACSR). This approach reformulates image-text matching with fine-grained semantic scarcity as a variational inference problem. It constructs a latent space for cross-modal similarity and uses regularization techniques to mitigate overfitting to binary annotations. Experiments on image-text retrieval, domain generalization, and base-to-novel generalization demonstrate the proposed method's effectiveness and robust generalization ability.

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