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:2608.29644v1 Announce Type: cross Abstract: Attributing an artwork to an artist has traditionally relied on detailed visual observations and descriptions, known as stylistic analysis in art his...
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: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.
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:2608. 06751v1 Announce Type: cross Abstract: Artist-grounded image generation requires more than appending an artist name to a prompt.
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.
AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices.
arXiv:2608. 10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents.
arXiv:2608. 11907v1 Announce Type: cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
arXiv:2608. 11907v2 Announce Type: replace-cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
MUSE is a new benchmark designed to evaluate large vision‑language models on artistic image understanding within situated educational contexts. It separates image annotation from question generation, offering twelve tasks that cover visual perception, semantic and affective interpretation, cultural understanding, and compositional reasoning across diverse artistic images from Singaporean, Southeast Asian, and Western traditions. The benchmark reveals significant gaps in model performance, especially in affective interpretation and compositional reasoning, and highlights common failure modes for trustworthy educational multimodal systems.
arXiv:2609.13348v1 Announce Type: cross Abstract: This paper presents ViFA-Council, a three-stage multi-agent framework that employs multiple large language models (LLMs) to tackle two culturally com...