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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Visual Question Answering (VQA) aims to comprehend questions and relevant image content for accurate answers.
    • Current VQA models often directly combine visual and question features, leading to a semantic gap and poor cross-modality alignment.
    • This misalignment hinders accurate matching of key visual content and limits VQA performance.

    Purpose of the Study:

    • To propose a novel model, the Caption Bridge-based Cross-modality Alignment and Contrastive learning model (CBAC), to address VQA semantic gap issues.
    • To enhance cross-modality semantic alignment and improve the accuracy of visual question answering.

    Main Methods:

    • Developed a CBAC model featuring a caption-based cross-modality alignment module and a visual-caption (V-C) contrastive learning module.
    • Utilized an auxiliary caption, semantically closer to the visual than the question, for pre-alignment feature generation.
    • Employed contrastive learning on V-C pairs to strengthen single-modality encoder alignment, leveraging stronger V-C semantic connections over Q-V pairs.

    Main Results:

    • The CBAC model significantly outperformed previous state-of-the-art VQA models on three benchmark datasets.
    • Ablation experiments confirmed the effectiveness and contribution of each module within the CBAC model.
    • Qualitative analysis using attention matrix visualization demonstrated the model's reasoning reliability.

    Conclusions:

    • The proposed CBAC model effectively reduces the semantic gap in VQA through caption-based alignment and contrastive learning.
    • CBAC demonstrates superior performance and enhanced semantic alignment capabilities compared to existing VQA methods.
    • The model offers a reliable approach for visual question answering by improving cross-modality understanding.