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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Cross-Modal Attention With Semantic Consistence for Image-Text Matching.

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    Summary

    This study introduces Cross-modal Attention with Semantic Consistency (CASC) for improved image-text matching. CASC enhances visual-semantic similarity by combining local alignment with global semantic consistency.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Image-text matching aims to quantify visual-semantic similarity.
    • Fine-grained methods use local region-word alignment but struggle with inaccurate detections or missing regions.
    • High-level semantic words may not map to single image regions, limiting local alignment effectiveness.

    Purpose of the Study:

    • To propose a novel hybrid matching approach, Cross-modal Attention with Semantic Consistency (CASC).
    • To address limitations of local alignment by incorporating global semantic consistency.
    • To enhance image-text matching performance through a joint framework.

    Main Methods:

    • Developed a joint framework, CASC, integrating cross-modal attention for local alignment.
    • Implemented multilabel prediction for global semantic consistency, extracting labels from existing corpora.
    • Used CASC to provide a global similarity constraint for aggregated region-word similarities.

    Main Results:

    • CASC effectively preserves global semantic consistency alongside local alignment.
    • Experiments on Flickr30k and MSCOCO datasets demonstrate superior performance.
    • Outperformed over 15 state-of-the-art image-text matching methods.

    Conclusions:

    • The proposed CASC model significantly improves image-text matching.
    • Combining local and global semantic information is crucial for robust matching.
    • CASC offers an effective solution for accurate visual-semantic similarity measurement.