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Related Experiment Video

Updated: Aug 19, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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GLCM: Global-Local Captioning Model for Remote Sensing Image Captioning.

Qi Wang, Wei Huang, Xueting Zhang

    IEEE Transactions on Cybernetics
    |November 29, 2022
    PubMed
    Summary

    This study introduces a global-local captioning model (GLCM) for remote sensing image captioning (RSIC). The model effectively combines global and local visual features, improving sentence generation and interpretability.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Remote Sensing

    Background:

    • Remote sensing image captioning (RSIC) is a challenging cross-modal task.
    • Existing methods often struggle to balance global context and local discriminative details.

    Purpose of the Study:

    • To propose an attention-based global-local captioning model (GLCM) for RSIC.
    • To enhance visual feature representation by integrating global and local features.
    • To improve the interpretability of RSIC.

    Main Methods:

    • Developed an attention-based global-local captioning model (GLCM).
    • Utilized both global features for comprehensive relevance and local features for word discrimination.
    • Visualized word-feature correlations for enhanced interpretability.

    Main Results:

    • Achieved comparable results on the UCM-captions dataset.
    • Demonstrated superior performance on the Sydney-captions and RSICD datasets.
    • The proposed GLCM provides interpretable insights into the captioning process.

    Conclusions:

    • The GLCM effectively leverages global-local visual features for improved RSIC.
    • The model offers enhanced interpretability through visualization.
    • The approach shows significant potential for advancing RSIC research.