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Attribute-Guided Attention for Referring Expression Generation and Comprehension.

Jingyu Liu, Wei Wang, Liang Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 17, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an attribute-guided attention model for referring expression generation and comprehension. The model uses visual attributes to improve object recognition in text and images, achieving state-of-the-art results.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Referring expressions are verbal cues used to identify specific objects.
    • Referring expression generation and comprehension are inverse tasks crucial for human-computer interaction.
    • Visual attributes are vital for disambiguating objects in complex scenes.

    Purpose of the Study:

    • To propose an attribute-guided attention model for enhancing referring expression generation and comprehension.
    • To leverage explicit attribute supervision signals for improved task performance.
    • To bridge the gap between visual and textual representations using attribute correspondence.

    Main Methods:

    • Developed an attribute-guided attention model incorporating explicit attribute supervision.
    • Integrated online predicted attributes as additional visual representations.
    • Designed an attribute-guided attention module to link visual and textual attribute counterparts.
    • Utilized attention mechanisms on visual features and word embeddings.

    Main Results:

    • Demonstrated significant improvements over baseline methods on RefCOCO, RefCOCO+, and RefCOCOg datasets.
    • Achieved results favorably comparable to state-of-the-art performance.
    • Validated the model's effectiveness through quantitative and qualitative analyses.
    • Confirmed the contribution of individual modules via ablation studies.

    Conclusions:

    • The proposed attribute-guided attention model effectively enhances referring expression tasks.
    • Explicit attribute supervision and attribute-guided attention are beneficial for both generation and comprehension.
    • The framework offers valuable insights and potential for future research in referring expression understanding.