Abstract4D：用于理解抽象艺术视觉语言的大规模数据集与框架

中文摘要 AI 辅助

人工智能能够对艺术风格进行分类并合成图像，但仍缺乏能赋予艺术意义的视觉语言模型。抽象绘画弱化了对象语义，突出了结构线索，是计算感知的理想测试平台。我们推出Abstract4D，这是迄今为止最大的抽象绘画数据集：超过12万张图像，搭配丰富元数据与多维提示，涵盖每件作品的感知属性——形态、色彩、纹理与构图。注释由人机视觉语言模型（VLM）混合流程生成，以保证质量与一致性。利用Abstract4D，我们开展两项工作：其一，通过大规模嵌入可视化分析抽象艺术的语义结构，揭示感知关系如何组织艺术意义；其二，建立分类、跨模态检索与文本到图像生成的基准任务，以评估AI模型感知与再现抽象视觉语言的能力。这些分析共同表明，Abstract4D可用于探索和定量评估AI表征与解读抽象艺术的能力。

英文摘要

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.

发表机构

• College of Computer Science, Sichuan University（四川大学计算机学院）

机构由 AI 辅助整理，请以论文原文为准。