arXiv AI
Conducting Stylistic Analysis of Paintings through an Art-History Agent
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arXiv AI
Aug 6
ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation
arXiv:2608. 05026v1 Announce Type: cross Abstract: High-quality annotation of artworks is essential for computational art research, yet extracting implicit semantics remains challenging due to the reliance on culturally grounded meanings and deep contextual knowledge behind the images.
arXiv Computer Vision
6d ago
Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art
Abstract4D is the largest dataset of abstract paintings, containing over 120,000 images with rich metadata and multi‑dimensional prompts that capture perceptual attributes such as form, color, texture, and composition. The dataset is annotated via a hybrid human–VLM pipeline to ensure quality and consistency. Using Abstract4D, the authors analyze the semantic structure of abstract art through large‑scale embedding visualization and establish benchmark tasks for classification, cross‑modal retrieval, and text‑to‑image generation to evaluate AI models’ perception and reproduction of abstract visual language.
arXiv Computation and Language
4d ago
ExpArt-KG: Artwork Image Description Generation through Iterative Exploration of Knowledge Graphs
The paper introduces ExpArt-KG, a knowledge graph tailored to the artwork domain, and a retrieval‑augmented generation framework that alternates between generating answers and retrieving relevant facts from the graph. By using a correctness judgment to guide the search, the method efficiently gathers the necessary factual information, improving the detail of image explanations while reducing external knowledge retrieval costs. Experimental results demonstrate that the approach maintains generation quality comparable to fixed‑iteration methods.
Hugging Face Trending Papers
Jul 22
Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches
The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks.
Hugging Face Trending Papers
Jul 9
ArtMine: Discovering and Formalizing Artistic Processes
Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems can synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the creative processes underlying their creation.
arXiv AI
Jul 10
ArtMine: Discovering and Formalizing Artistic Processes
arXiv:2607. 08331v1 Announce Type: cross Abstract: Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production.