arXiv 2604.196892026-04-21cs.AI

A-MAR：基于代理的多模态艺术检索用于细粒度艺术作品理解

A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding

University of Amsterdam（阿姆斯特丹大学）；University of Bristol（布里斯托大学）；Amazon AGI（亚马逊人工智慧）；College of Business and Economics（商学院和经济学学院）；University of Johannesburg（约翰内斯堡大学）

Shuai Wang, Hongyi Zhu, Jia-Hong Huang, Yixian Shen, Chengxi Zeng, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring

发表信息：: ICMR 2026, ACM International Conference on Multimedia Retrieval

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AI总结：

本文提出A-MAR框架，通过结构化推理计划显式指导多模态艺术检索，提升解释质量和证据 grounding 能力，在艺术领域验证了代理式多模态推理的有效性。

摘要：

理解艺术作品需要对视觉内容和文化、历史及风格背景进行多步骤推理。尽管最近的多模态大语言模型在艺术解释方面表现出色，但它们依赖于隐含推理和内部化知识，限制了可解释性和显式证据支撑。我们提出了A-MAR，一种基于代理的多模态艺术检索框架，该框架将检索条件显式地设定在结构化推理计划上。给定一个艺术作品和用户查询，A-MAR首先将任务分解为结构化推理计划，该计划指定了每个步骤的目标和证据要求。然后检索条件基于此计划进行，使能够有针对性地选择证据并支持逐步、基于证据的解释。为了评估艺术领域中的代理多模态推理，我们引入了ArtCoT-QA。这个诊断基准具有多步骤推理链，适用于各种艺术相关查询，使能够进行更细致的分析，超越简单的最终答案准确性。在SemArt和Artpedia上的实验表明，A-MAR在最终解释质量上始终优于静态、非计划检索和强大的MLLM基线，而ArtCoT-QA的评估进一步展示了其在证据 grounding 和多步骤推理能力方面的优势。这些结果突显了基于推理的检索在知识密集型多模态理解中的重要性，并将A-MAR定位为可解释、以目标驱动的AI系统的一个步骤，尤其对文化产业具有特别相关性。代码和数据可在：https://github.com/ShuaiWang97/A-MAR。

英文摘要：

Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting interpretability and explicit evidence grounding. We propose A-MAR, an Agent-based Multimodal Art Retrieval framework that explicitly conditions retrieval on structured reasoning plans. Given an artwork and a user query, A-MAR first decomposes the task into a structured reasoning plan that specifies the goals and evidence requirements for each step. Retrieval is then conditionedon this plan, enabling targeted evidence selection and supporting step-wise, grounded explanations. To evaluate agent-based multi- modal reasoning within the art domain, we introduce ArtCoT-QA. This diagnostic benchmark features multi-step reasoning chains for diverse art-related queries, enabling a granular analysis that extends beyond simple final answer accuracy. Experiments on SemArt and Artpedia show that A-MAR consistently outperforms static, non planned retrieval and strong MLLM baselines in final explanation quality, while evaluations on ArtCoT-QA further demonstrate its advantages in evidence grounding and multi-step reasoning ability. These results highlight the importance of reasoning-conditioned retrieval for knowledge-intensive multimodal understanding and position A-MAR as a step toward interpretable, goal-driven AI systems, with particular relevance to cultural industries. The code and data are available at: https://github.com/ShuaiWang97/A-MAR.

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