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Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution
Jin Kim1,2, Byunghwee Lee3,4, Taekho You5
1Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea.
Summary
Multimodal generative AI reveals that contextual art elements, like subjects, better align with historical periods than formal ones. This approach quantizes cultural knowledge by integrating temporal and historical contexts into art analysis.
Area of Science:
- Art History
- Artificial Intelligence
- Computational Creativity
Background:
- Multimodal generative AI offers new avenues for analyzing large-scale artworks.
- Previous research has primarily explored AI's creative potential, neglecting its representational capabilities in latent spaces.
- Understanding how AI models represent art is crucial for advancing computational art analysis.
Purpose of the Study:
- To investigate generative AI's capacity to represent artworks in latent spaces.
- To compare the representation of formal versus contextual aspects of paintings within AI models.
- To explore the integration of temporal, cultural, and historical contexts in art analysis.
Main Methods:
- Utilized Stable Diffusion, a generative AI model, to analyze 500 years of Western paintings.
- Extracted two types of latent information: formal aspects (e.g., colors) and contextual aspects (e.g., subjects).
- Conducted a generative experiment to validate findings by synthesizing artworks with infused historical contexts.
Main Results:
- Contextual information demonstrated stronger vector alignment and orientation with artistic periods, styles, and artists compared to formal elements.
- Artistic expression was shown to align with historical shifts through extracted contextual keywords.
- Generated artworks successfully matched the stylistic patterns of target historical periods, validating the model's understanding.
Conclusions:
- Multimodal AI significantly expands traditional art analysis by incorporating diverse contextual information.
- Generative AI can quantify the latent structure of cultural knowledge embedded within artworks.
- This approach offers a novel method for integrating temporal, cultural, and historical dimensions into art historical research.
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