Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art
Artificial intelligence can classify artistic styles and synthesize images, but it still lacks a model of the visual language that gives art meaning. Abstract painting minimizes object semantics and foregrounds structural cues, making it an ideal testbed for computational perception. We introduce \textbf{Abstract4D}, the largest dataset of abstract paintings to date: more than 120,000 images paired with rich metadata and multi-dimensional prompts that capture each work's perceptual attributes---\textit{form, color, texture, and composition}. Annotations are produced by a hybrid human--VLM pipe
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- PossiblePossibly related (embedding) · 52%What Makes AI Art Worth Collecting? →
- PossiblePossibly related (embedding) · 52%Show HN: Painterly – Turn pictures into digital paintings without generative AI →
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- LinkedLinked via arxiv author · 85%Shaowei Zhang →
“Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art”
- LinkedLinked via arxiv author · 85%Yuanpei Zhao →
“Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art”
- LinkedLinked via arxiv author · 85%Ji-Zhe Zhou →
“Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art”
- LinkedLinked via arxiv author · 85%Mao Li →
“Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art”