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    <abstract>Recent advancements in text-to-image generation, notably the series of Stable Diffusion methods, have enabled the production of diverse, high-quality photo-realistic images. Nevertheless, these techniques still exhibit limitations in terms of knowledge access. Retrieval-augmented image generation is a straightforward way to tackle this problem. Current studies primarily utilize coarse-grained retrievers, employing initial prompts as search queries for knowledge retrieval. This approach, however, is ineffective in accessing valuable knowledge in long-tail text-to-image generation scenarios. To alleviate this problem, we introduce FineRAG, a fine-grained model that systematically breaks down the retrieval-augmented image generation task into four critical stages: query decomposition, candidate selection, retrieval-augmented diffusion, and self-reflection. Experimental results on both general and long-tailed benchmarks show that our proposed method significantly reduces the noise associated with retrieval-augmented image generation and performs better in complex, open-world scenarios.</abstract>
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%0 Conference Proceedings
%T FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation
%A Yuan, Huaying
%A Zhao, Ziliang
%A Wang, Shuting
%A Xiao, Shitao
%A Ni, Minheng
%A Liu, Zheng
%A Dou, Zhicheng
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yuan-etal-2025-finerag
%X Recent advancements in text-to-image generation, notably the series of Stable Diffusion methods, have enabled the production of diverse, high-quality photo-realistic images. Nevertheless, these techniques still exhibit limitations in terms of knowledge access. Retrieval-augmented image generation is a straightforward way to tackle this problem. Current studies primarily utilize coarse-grained retrievers, employing initial prompts as search queries for knowledge retrieval. This approach, however, is ineffective in accessing valuable knowledge in long-tail text-to-image generation scenarios. To alleviate this problem, we introduce FineRAG, a fine-grained model that systematically breaks down the retrieval-augmented image generation task into four critical stages: query decomposition, candidate selection, retrieval-augmented diffusion, and self-reflection. Experimental results on both general and long-tailed benchmarks show that our proposed method significantly reduces the noise associated with retrieval-augmented image generation and performs better in complex, open-world scenarios.
%U https://aclanthology.org/2025.coling-main.741/
%P 11196-11205

Markdown (Informal)

[FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation](https://aclanthology.org/2025.coling-main.741/) (Yuan et al., COLING 2025)

• FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation (Yuan et al., COLING 2025)

ACL

• Huaying Yuan, Ziliang Zhao, Shuting Wang, Shitao Xiao, Minheng Ni, Zheng Liu, and Zhicheng Dou. 2025. FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 11196–11205, Abu Dhabi, UAE. Association for Computational Linguistics.