ArtiMuse：联合评分与专家级理解的细粒度图像美学评估
ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding
提出基于多模态大语言模型的联合评分与专家级理解框架，并构建首个专家标注的细粒度美学评估数据集，为视觉美学分析提供可复用的模型与数据基础。
原文依据 ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论教育应用、艺术创作和人工智能生成内容（AIGC）技术的快速发展，大幅提升了对全面图像美学评估（IAA）的实际需求，尤其要求方法能够同时提供定量评分和专业理解。基于多模态大语言模型（MLLM）的IAA方法相比传统方法展现出更强的感知和泛化能力，但它们存在模态偏差（仅评分或仅文本）且缺乏细粒度属性分解，从而无法支持进一步的美学评估。本文提出：（1）ArtiMuse，一种创新的基于MLLM的IAA模型，具备联合评分与专家级理解能力；（2）ArtiMuse-10K，首个由专家策划的IAA数据集，包含10,000张图像，涵盖5个主类别和15个子类别，每张图像均由专业专家标注了8维属性分析和整体评分。模型和数据集都将公开。
英文原文摘要 1068 字符
The rapid advancement of educational applications, artistic creation, and AI-generated content (AIGC) technologies has substantially increased practical requirements for comprehensive Image Aesthetics Assessment (IAA), particularly demanding methods capable of delivering both quantitative scoring and professional understanding. Multimodal Large Language Model (MLLM)-based IAA methods demonstrate stronger perceptual and generalization capabilities compared to traditional approaches, yet they suffer from modality bias (score-only or text-only) and lack fine-grained attribute decomposition, thereby failing to support further aesthetic assessment. In this paper, we present: (1) ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities; (2) ArtiMuse-10K, the first expert-curated IAA dataset comprising 10,000 images spanning 5 main categories and 15 subcategories, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score. Both the model and dataset will be made public.