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Related Experiment Video

Updated: Jul 9, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Self-Paced Multi-Grained Cross-Modal Interaction Modeling for Referring Expression Comprehension.

Peihan Miao, Wei Su, Gaoang Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework for referring expression comprehension (REC) that effectively integrates multi-grained visual and linguistic information. The model enhances language-to-vision localization by adaptively learning from complex examples.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Referring expression comprehension (REC) is a challenging vision-language task.
    • Accurate REC requires integrating multi-grained information from visual and linguistic modalities.
    • Handling diverse visual scenes and linguistic variations, especially in hard examples, is crucial.

    Purpose of the Study:

    • To propose a novel framework for enhancing language-to-vision localization in REC.
    • To effectively aggregate multi-grained information from different modalities.
    • To improve the model's ability to learn from hard examples with abundant information.

    Main Methods:

    • A Self-paced Multi-grained Cross-modal Interaction Modeling framework is proposed.
    • A transformer-based multi-grained cross-modal attention mechanism is designed.
    • A self-paced sample informativeness learning strategy is introduced to handle sample variance.

    Main Results:

    • The proposed framework significantly outperforms existing state-of-the-art methods.
    • Demonstrated effectiveness on widely used datasets: RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame.
    • The innovations in network structure and learning mechanism contribute to improved performance.

    Conclusions:

    • The Self-paced Multi-grained Cross-modal Interaction Modeling framework effectively addresses challenges in REC.
    • The method successfully aggregates multi-grained information and learns from hard examples.
    • The approach shows significant improvements in language-to-vision localization tasks.