Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art

2026-08-28

AI Summary

研究通过创建大规模抽象艺术数据集Abstract4D，利用混合人类-VLM管道注释，分析抽象艺术的视觉语言结构，并设立基准任务评估AI模型对此类艺术的理解与再现能力。

Abstract

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 pipeline for quality and consistency. Using Abstract4D, we (i) analyze the semantic structure of abstract art through large-scale embedding visualization, uncovering how perceptual relationships organize artistic meaning, and (ii) establish benchmark tasks for classification, cross-modal retrieval, and text-to-image generation to evaluate how AI models perceive and reproduce abstract visual language. Together, these analyses demonstrate how Abstract4D enables both exploration and quantitative assessment of AI's ability to represent and interpret abstract art.

Problem

• visual language
• abstract art
• perceptual attributes
• artistic meaning
• computational perception

Innovation

• Abstract4D
• visual language model (VLM)
• perceptual attributes
• large-scale embedding visualization
• cross-modal retrieval