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Origami Inspired Self-assembly of Patterned and Reconfigurable Particles
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Panel-Page-Aware Comic Genre Understanding.

Chenshu Xu, Xuemiao Xu, Nanxuan Zhao

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    This summary is machine-generated.

    This study introduces a novel comic genre classification model that understands panel and page relationships. The model effectively predicts comic genres, outperforming existing methods on a new dataset.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Digital Media Analysis

    Background:

    • Digital visual media traditionally uses still images for storytelling.
    • Rapid growth in digital media necessitates efficient topic and genre identification.
    • Existing image analysis methods fail with comics due to panel divisions and visual inconsistency.

    Purpose of the Study:

    • To develop a model for accurate comic genre classification.
    • To address the limitations of natural image analysis techniques in the comic domain.
    • To leverage long-term semantic understanding of comic sequences for genre identification.

    Main Methods:

    • Proposed a Panel-Page-aware Comic genre classification model ([Formula: see text]Comic).
    • Utilized detected panel boxes for panel representation extraction.
    • Employed self-attention mechanisms for panel-page understanding and interdependent classifiers for label correlation.

    Main Results:

    • Developed the first comic dataset for multi-genre classification.
    • Achieved superior performance compared to state-of-the-art methods.
    • Demonstrated the model's extensibility to multi-modal scenarios and its practicability for whole comic books.

    Conclusions:

    • The [Formula: see text]Comic model effectively classifies comic genres by analyzing panel-page relationships.
    • The proposed approach advances comic image analysis and genre prediction.
    • This work provides a robust solution for understanding and categorizing digital comic content.