当文化相遇：多文化文本到图像生成
When Cultures Meet: Multicultural Text-to-Image Generation
AI总结：
本文提出多文化文本到图像生成任务，构建首个评估基准，分析先进模型在多文化场景下的表现，提出MosAIG框架提升图像生成质量与文化准确性。
摘要：
文本到图像生成模型在文化同质性环境下已取得显著成效，但其生成多文化场景——其中人物和地标来自不同文化的能力仍鲜有探索。我们引入多文化文本到图像生成作为新任务，并提出首个评估基准以研究此设定。我们的数据集包含涵盖五个国家、三个年龄段、两种性别、25个历史地标和五种语言的9000张图像。利用此基准，我们从对齐、图像质量、审美、知识和公平性等多个维度分析了最先进的文本到图像模型的行为。作为整合文化和人口信息的一种策略，我们探索了MosAIG，一种多代理框架，通过利用具有不同文化身份的LLMs来增强多文化图像生成。我们的分析表明，更丰富的提示组成相比简单的提示能提高图像质量和文化准确性，同时揭示了语言和人口群体间的显著差异。我们在此发布数据集和代码至https://github.com/AIM-SCU/MosAIG。
英文摘要：
Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultural scenes, where people and landmarks originate from different cultures, remains largely unexplored. We introduce multicultural text-to-image generation as a new task and present the first benchmark designed to study this setting. Our dataset contains 9,000 images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages. Using this benchmark, we analyze the behavior of state-of-the-art text-to-image models across multiple dimensions, including alignment, image quality, aesthetics, knowledge, and fairness. As one strategy for composing cultural and demographic information, we explore MosAIG, a Multi-Agent framework that enhances multicultural Image Generation by leveraging LLMs with distinct cultural personas. Our analysis shows that richer prompt composition can improve image quality and cultural grounding compared to simple prompts, while revealing substantial disparities across languages and demographic groups. We release our dataset and code at https://github.com/AIM-SCU/MosAIG.
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。
