Conducting Stylistic Analysis of Paintings through an Art-History Agent

• URL: http://arxiv.org/abs/2608.29644v1
• Date: Sun, 30 Aug 2026 08:10:58 GMT
• Title: Conducting Stylistic Analysis of Paintings through an Art-History Agent
• Abstract summary: We present an AI framework that automates stylistic analysis of paintings.<n>By training a vision transformer (ViT) on a large corpus of paintings with metadata, our system encodes this art history-specific data as embeddings.<n>A large language model (LLM) then interprets each feature by retrieving associated artworks and their accompanying curator-written texts, and synthesizes them into descriptions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attributing an artwork to an artist has traditionally relied on detailed visual observations and descriptions, known as stylistic analysis in art history. By contrast, current artificial intelligence (AI) models used in the field offer only unexplained probabilistic classifications. To bridge this methodological gap, we present an AI framework that automates stylistic analysis of paintings, providing a foundation for enhancing evidence collection, discovery, and verification. By training a vision transformer (ViT) on a large corpus of paintings with metadata, our system encodes this art history-specific data as embeddings. These representations are factorized via sparse dictionary learning into a shared set of features that recur across the training set. A large language model (LLM) then interprets each feature by retrieving associated artworks and their accompanying curator-written texts, and synthesizes them into descriptions that reflect their stylistic attributes. Finally, an autonomous coordinator LLM applies a reasoning-and-action (ReAct) framework to weight, test, and refine these features into cohesive descriptions of an artwork, or comparisons of artworks. This approach converts detailed visual features into descriptive terms, addressing a key challenge in art history. It thus connects the use of images as data with the semantic concerns of humanists, establishing vision-based computational art history as an area for future growth.

Related papers

• Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI [70.05655131235999]
  Explainable Authorship Variational Autoencoder (EAVAE) is a novel framework that explicitly disentangles style from content.<n>Disentanglement is enforced through a novel discriminator.<n>EAVAE excels in few-shot learning over the M4 dataset for AI-generated text detection.
  arXiv  Detail & Related papers  (2026-04-23T05:34:20Z)
• Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style [60.562013130566726]
  We characterize the mechanisms underlying VLMs' ability to predict artistic style.<n>We employ a latent-space decomposition approach to identify concepts that drive art style prediction.<n>In cases where an irrelevant concept was used to successfully predict style, art historians identified possible reasons for its success.
  arXiv  Detail & Related papers  (2026-03-11T17:49:45Z)
• Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment [51.40989269202702]
  aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC.<n>We propose ArtQuant, an aesthetics assessment framework for artistic images which couples isolated aesthetic dimensions through description generation.<n>Our approach achieves epoch state-of-the-art performance on several datasets while requiring only 33% of conventional trainings.
  arXiv  Detail & Related papers  (2025-12-29T12:18:26Z)
• ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding [21.469839845171652]
  ArtRAG is a novel framework that combines structured knowledge with retrieval-augmented generation (RAG) for multi-perspective artwork explanation.<n>At inference time, a structured retriever selects semantically and topologically relevant subgraphs to guide generation.<n>Experiments on the SemArt and Artpedia datasets show that ArtRAG outperforms several heavily trained baselines.
  arXiv  Detail & Related papers  (2025-05-09T13:08:27Z)
• KALE: An Artwork Image Captioning System Augmented with Heterogeneous Graph [24.586916324061168]
  We present KALE Knowledge-Augmented vision-Language model for artwork Elaborations. KALE incorporates the metadata in two ways: firstly as direct textual input, and secondly through a multimodal heterogeneous knowledge graph. Experimental results demonstrate that KALE achieves strong performance over existing state-of-the-art work across several artwork datasets.
  arXiv  Detail & Related papers  (2024-09-17T06:39:18Z)
• Have Large Vision-Language Models Mastered Art History? [3.790400719319821]
  We test whether Vision-Language Models (VLMs) can classify the style, author and creation date of paintings.<n>Art historians have long studied the unique aspects of artworks, with style prediction being a crucial component of their discipline.
  arXiv  Detail & Related papers  (2024-09-05T13:33:57Z)
• GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
  Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
  arXiv  Detail & Related papers  (2024-08-01T11:52:56Z)
• Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
  ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists. We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
  arXiv  Detail & Related papers  (2023-05-08T17:58:27Z)
• Language Does More Than Describe: On The Lack Of Figurative Speech in Text-To-Image Models [63.545146807810305]
  Text-to-image diffusion models can generate high-quality pictures from textual input prompts. These models have been trained using text data collected from content-based labelling protocols. We characterise the sentimentality, objectiveness and degree of abstraction of publicly available text data used to train current text-to-image diffusion models.
  arXiv  Detail & Related papers  (2022-10-19T14:20:05Z)
• Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors [20.98603643788824]
  Image compositions are useful in analyzing the interactions in an image to study artists and their artworks. In this work, we attempt to automate this process using the existing state of the art machine learning techniques. Our approach focuses on two central themes of image composition: (a) detection of action regions and action lines of the artwork; and (b) pose-based segmentation of foreground and background.
  arXiv  Detail & Related papers  (2020-09-08T15:01:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.