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

doi:10.1109/TVCG.2026.3653892

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

2026-01-16

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DOI：10.1109/TVCG.2026.3653892

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Figurative art, as a culturally embedded medium, encodes narrative, symbolic, and emotional meanings that reflect artistic choices and historical realities. The recent availability of large-scale digital collections of figurative artworks creates opportunities for computational analysis, but existing methods mostly focus on classification or style detection, lacking structured modeling of high-level figurative elements and integration of cultural context. We present DAVA, a visual analytics system that supports interdisciplinary exploration of figurative art. First, we model paintings across three structural levels: facial expressions (micro), posture features (meso), and object co-occurrence (macro). Second, we employ a vision–language model to discover latent patterns from these features and present them through novel visualization designs. Third, we introduce domain-informed AI agents that simulate interdisciplinary research teams to interpret artworks in cultural and historical context. To evaluate DAVA, we first conducted a quantitative evaluation demonstrating the accuracy and consistency of the multi-agent interpretation mechanism. Case studies and expert interviews then confirmed the system’s utility and support for semantically and historically informed exploration of figurative art.

Keywords:

Visual analytics

digital humanities

art interpretation

large language model

AI agent

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

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