Aesthetic Perception and Analysis

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• Feed the Panel Dimensions, Not Verdicts: Rubric-Decomposed Fusion of Vision-Language Aesthetic Judges

  Amit Jadhav, Shaurya Beriwala, Beomjin Kim · 24 septembre 2026

  Vision-language models (VLMs) are deployed as zero-shot judges of image aesthetics, and panels of several models are recommended, on thin evidence, as the way to make such judges reliable. On two human-rated datasets, EVA and PARA, we find that a panel of holistic judges never significantly beats it…

• Moonworks Lunara: Modeling Artistic Intelligence

  Yan Wang, Yanzu Wang, Maitreyee Joshi, Samiha Sadeka, Partho Hassan, Reza Jarral, Sayeef Abdullah, Sabit Hassan · 22 septembre 2026

  We formulate \emph{Artistic Intelligence} as exploration driven world realization, leaving space for creative possibility while preserving the semantic, artistic, and compositional structure that must remain true. Moonworks Lunara, a text-to-image model, implements this framework with a novel Diffus…

• Fragment-Aware Vision Transformers for Fresco-Fragment Style Classification

  Sara Miketek, Biagio Barchielli, Nadeem Iqbal Kajla, Sinem Aslan · 21 septembre 2026

  Artistic style classification is usually studied on complete artworks, where models can exploit global composition, spatial organisation, and iconographic structure. In archaeological settings, however, artworks often survive only as fragmented remains, forcing recognition from incomplete, irregular…

• Automated Goldsmith's Mark Retrieval in Silverware

  Atmik Tiwari, Vincent Christlein, Mark Fichtner, Freya Gohlke, Birgit Sch\"ubel, Theresa Witting, Heike Zech, Mathias Zinnen · 18 septembre 2026

  For art historians, goldsmith marks play a critical role in the identification and dating of artifacts. In practice, experts must manually compare a query mark against hundreds of documented examples, a process that is both tedious and highly dependent on specialist knowledge. To address this, we pr…

• Neo-Classic: A Benchmark for Evaluating Linguistic-Aesthetic Reasoning in Classical Chinese Poetry

  Han Zhang, Zihan Gu, Zhiyuan Wang, Tianyi Ma, Jiacheng Lu, Xinyan Zhang, Yuhao Wei, Cheng Hua · 18 septembre 2026

  While Large Language Models (LLMs) achieve high accuracy on established Classical Chinese Poetry benchmarks, it remains challenging to distinguish transferable Linguistic-Aesthetic Reasoning from reliance on familiar pre-training patterns. To address this issue, we introduce Neo-Classic, an evaluati…

• MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education

  Luyao Zhu, Xun Wei Yee, Wei Li, Mun Thye Mak, Wee Siong Ng · 17 septembre 2026

  Large vision-language models have achieved remarkable progress in multi-modal understanding, yet their capabilities in educational settings remain insufficiently evaluated. In AI-assisted language learning, models must interpret artistic imagery, understand its semantic, affective, and cultural cont…

• Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM

  Ahmed Ammar Kubba, Manar Abu Talib, Iman Ibrahim, Qassim Nasir · 16 septembre 2026

  The analysis and classification of cultural heritage architectural styles remain challenging due to the complexity of visual images of buildings, which are highly relied on in traditional CNN-based classification approaches in comparison to textual descriptions, and the relative lack of non-western …

• MegaStyle++: Scaling Image Style Space through Hierarchical Style Definition

  Junyao Gao, Sibo Liu, Jiaxing Li, Yanan Sun, Weidong Zhang, Cairong Zhao, Jun Zhang · 2 septembre 2026

  Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified and explicit definition of image style remains lacking. In this work, we first discuss the fundamental question of what is style and then propose a …

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

  Haowei Zhang, Yuanpei Zhao, Ji-Zhe Zhou, Mao Li · 31 août 2026

  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 \t…

• AesCanvas: A Large-Scale Dataset and Benchmark for Aesthetic Critique and Contextual Suitability

  Xuanwei Hu, Haoyu Dong, Kejun Wu, Tianyi Liu, Jianjun Gao · 28 août 2026

  Recent advances in Multimodal Large Language Models (MLLMs) have extended Image Aesthetic Assessment (IAA) beyond scalar scores toward interpretable critique and guidance. Yet existing benchmarks mainly assess intrinsic visual quality or fixed domain criteria, leaving open whether an appealing image…

• Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces

  Luca Bux, Thiago Rios, Ingo Scholtes, Stefan Menzel · 27 août 2026

  Generative design interfaces increasingly expose semantic controls that let users steer output with concepts such as "more elegant" or "more minimalist," typically encoded by a vision-language model (VLM). A practical question is whether state-of-the-art VLMs represent objects consistently in terms …

• On the Separation of Human and AI-Generated Images in CLIP Embedding Space

  Andrea Asperti · 27 août 2026

  We identify a previously unreported phenomenon in CLIP representations: human and AI-generated paintings spontaneously separate along the dominant principal directions of their joint embedding distribution, without any supervised objective designed to distinguish the two classes. Rather than exploit…

• Fidelity Preference, Not Demographic Preference: A Pixel-Level Attribute-Sensitivity Audit of Image Aesthetic/Preference Scorers

  Mingyang Xu · 26 août 2026

  Text-to-image systems use learned aesthetic scorers to filter training data and guide generation, but whether these scores encode demographic attributes as objective quality is unclear. We audit four scorers (LAION-Aesthetics, PickScore, ImageReward, HPSv2) using pixel-level interventions on skin to…

• Uncertainty-Aware Art-Historical Dating with Vision-Language Models

  Stefanie Schneider, Peter Bell · 20 août 2026

  Museum and archival datasets do not mirror historical artistic production, but materialize the contingent histories of collecting, preservation, cataloging, and digitization. This has direct consequences for interpreting pretrained image representations: they may appear to encode historical time whi…

• PALATE: Personalized Aesthetic Learning through Adaptive Taste Evolution for Multi-User Portrait Retouching

  Jingxuan Wang, Yifan Mei, Yuxia Niu, Chaowan Jiao, Qijin Shen · 20 août 2026

  Automatic portrait retouching has advanced rapidly, yet its objective is inherently subjective: the same portrait admits multiple professionally valid results, and users disagree about which one is best. Most existing methods optimize a population-level aesthetic standard and therefore cannot captur…

• Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction

  Zhaoxi Wei, Hongye Yang, Shuyuan Tian · 20 août 2026

  Amid concerns that generative AI may standardize art interpretation, this paper examines whether LLM-based interaction can support plural art-historical narrative construction. We present Sanyu Studio, a multi-agent dialogue system that models 321 Sanyu oil paintings as agents with fact, interpretat…

• ProFocus: Interpreting Affective Experience in Artistic Images with Progressive Visual Focusing

  Zhiyan Zhang, Zicheng Yan, Jianqi Chen, Peipei Song, Shanshan Wang, Xun Yang · 17 août 2026

  Interpreting the emotional responses triggered by images is central to achieving emotional intelligence. Compared with natural images, visual art is intentionally created to elicit emotional responses from its viewers through abstract concepts and visual metaphors, making affective interpretation pa…

• Style or Signature? Artist-Disjoint Evaluation of Style Classification in Frozen Vision Embeddings

  Rory Ashton · 17 août 2026

  Frozen image embeddings from models such as CLIP are increasingly used to classify paintings by art-historical style, with high reported accuracy. We ask whether this accuracy reflects an understanding of style or the recognition of individual artists. Standard evaluation uses random splits in which…

• From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Framework

  Wen-Fan Wang, TsaiHsuan Lin, Chi-Lan Yang, An-Ru Cheng, Bing-Yu Chen · 17 août 2026

  Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation. While generative AI (GenAI) can reproduce styles with high fidelity, current tools provide limited support for exploring new stylistic directions and may encourage sty…

• TangPoetryBench: A Multi-Dimensional Benchmark and Rubric-Conditioned Evaluator for Poetry-to-Image Generation

  Haoqi Hu, Tongji Luo, Li Zhang, Boning Zhou · 13 août 2026

  Text-to-image (T2I) models are increasingly asked to illustrate literary and cultural content, yet we cannot measure how well an image renders the meaning of a poem. The task is many-sided: a good illustration must be visually sound, faithful to the poem's imagery and scene, culturally and stylistic…

• MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding

  Shuai Wang, Wangyuan Ding, Yixian Shen, Jia-Hong Huang, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring · 12 août 2026

  Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surface content but struggle with formal analysis, grounded historical interpretation, or affective characterization. We argue this is not only a model but a…

• COMEX: A Composition-Grounded Benchmark and Learning Framework for Explainable Aesthetic Image Cropping

  Rui Yang, Wei Zhou, Dingyong Gou, Xiaohui Cui, Cong Li, Yinyin Gong, Yipo Huang, Jiliang Zhao · 11 août 2026

  Explainable aesthetic image cropping requires not only localizing a visually pleasing crop but also explaining why it is preferred. Existing crop-and-explain methods largely treat explanation as post-hoc text generation and overlook composition, a key aesthetic factor that links crop decisions with …

• Beyond Starry Night: Shortcut-Aware Control-State Planning for Artist-Grounded Text to Image Generation

  Kuan Xing, Ye Wang, Changyi Gan, Yuheng Li, Thao Nguyen, Yi Chang, Yilin Wang · 10 août 2026

  Artist-grounded image generation requires more than appending an artist name to a prompt. Image models often respond to artist names through canonical shortcuts, such as recurring motifs, generic palettes, or overrepresented period signatures, rather than preserving the user's intended scene. We int…

• Learning visual representations for compositional analysis of artworks and photographs

  Fatemeh Behrad, Tinne Tuytelaars, Johan Wagemans · 7 août 2026

  Composition, the deliberate arrangement of visual elements, is central to how meaning, emotion, and aesthetic quality are conveyed in artwork, yet it remains among the least formalized dimensions of visual understanding. Prior work highlights a persistent gap in learning meaningful compositional rep…

• ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation

  Xiaoyan Gu, Yifang Wang, Wenqing Zheng, Haozhong Liu, Yixia Zheng, Peiyi Jiang, Wenjie Ning, Wei Zhang, Wei Chen · 6 août 2026

  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. Current AI-assisted annotation tools often lack assistance or re…