AesTest：从感知到生产的美学智能测量
AesTest: Measuring Aesthetic Intelligence from Perception to Production
AI总结：
本文提出 AesTest 综合基准，通过十个任务、多源数据和多种美学查询类型，系统评估多模态大语言模型从感知到生产的美学智能，并揭示现有模型在该领域的重大挑战。
摘要：
感知和产生美学判断是多模态大语言模型（MLLMs）的一项基础性但尚未得到充分探索的能力。然而，现有的图像美学评估（IAA）基准在感知范围上较为狭窄，或缺乏评估系统性美学生产所需的多样性。为填补这一空白，我们引入了 AesTest，一个用于多模态美学感知与生产的综合基准，其特点如下：1）它由经过策划的多项选择题组成，涵盖十个任务，涉及感知、欣赏、创作和摄影。这些任务以生成学习的心理学理论为基础。2）它整合了来自多种来源的数据，包括专业编辑工作流程、摄影构图教程和众包偏好。它确保既覆盖专家级原则，也覆盖现实世界的变化。3）它支持各种美学查询类型，例如基于属性的分析、情感共鸣、构图选择和风格推理。我们在 AesTest 上评估了经过指令微调的 IAA 多模态大语言模型和通用多模态大语言模型，揭示了构建美学智能所面临的重大挑战。我们将公开发布 AesTest，以支持该领域未来的研究。
英文摘要：
Perceiving and producing aesthetic judgments is a fundamental yet underexplored capability for multimodal large language models (MLLMs). However, existing benchmarks for image aesthetic assessment (IAA) are narrow in perception scope or lack the diversity needed to evaluate systematic aesthetic production. To address this gap, we introduce AesTest, a comprehensive benchmark for multimodal aesthetic perception and production, distinguished by the following features: 1) It consists of curated multiple-choice questions spanning ten tasks, covering perception, appreciation, creation, and photography. These tasks are grounded in psychological theories of generative learning. 2) It integrates data from diverse sources, including professional editing workflows, photographic composition tutorials, and crowdsourced preferences. It ensures coverage of both expert-level principles and real-world variation. 3) It supports various aesthetic query types, such as attribute-based analysis, emotional resonance, compositional choice, and stylistic reasoning. We evaluate both instruction-tuned IAA MLLMs and general MLLMs on AesTest, revealing significant challenges in building aesthetic intelligence. We will publicly release AesTest to support future research in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
