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DAVA: Decoding Art With Visual Analytics Through Feature Modeling and Multi-Agent Collaboration.

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    This study introduces DAVA, a visual analytics system for exploring figurative art. DAVA models artworks on multiple levels and uses AI agents to interpret them within their cultural context.

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    Area of Science:

    • Art History
    • Computer Science
    • Digital Humanities

    Background:

    • Figurative art contains rich narrative, symbolic, and emotional meanings.
    • Computational analysis of art is limited, often focusing on classification and style detection, neglecting high-level elements and cultural context.
    • Large digital art collections offer new avenues for computational art analysis.

    Purpose of the Study:

    • To present DAVA, a visual analytics system for interdisciplinary exploration of figurative art.
    • To enable structured modeling of high-level figurative elements and integrate cultural context into computational art analysis.
    • To support semantically and historically informed art exploration.

    Main Methods:

    • Modeling paintings across facial expressions (micro), posture features (meso), and object co-occurrence (macro).
    • Utilizing a vision-language model to discover latent patterns from these features.
    • Developing domain-informed AI agents to simulate interdisciplinary research teams for artwork interpretation.
    • Designing novel visualizations to present discovered patterns.

    Main Results:

    • Quantitative evaluation demonstrated the accuracy and consistency of the multi-agent interpretation mechanism.
    • Case studies and expert interviews confirmed DAVA's utility.
    • The system effectively supports exploration of figurative art within cultural and historical contexts.

    Conclusions:

    • DAVA enhances interdisciplinary research by integrating computational analysis with domain expertise.
    • The system provides a novel approach to understanding the complex meanings encoded in figurative art.
    • DAVA facilitates a deeper, context-aware exploration of visual culture through advanced AI and visualization techniques.