@inproceedings{wang-etal-2025-rico,
title = "{RICO}: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction",
author = "Wang, Yuchi and
Cai, Yishuo and
Ren, Shuhuai and
Yang, Sihan and
Yao, Linli and
Liu, Yuanxin and
Zhang, Yuanxing and
Wan, Pengfei and
Sun, Xu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1105/",
pages = "21796--21815",
ISBN = "979-8-89176-332-6",
abstract = "Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimodal large language models (MLLMs) to enhance textual descriptions, but often suffer from inaccuracies due to hallucinations and incompleteness caused by missing fine-grained details. To address these limitations, we propose RICO, a novel framework that refines captions through visual reconstruction. Specifically, we leverage a text-to-image model to reconstruct a caption into a reference image, and prompt an MLLM to identify discrepancies between the original and reconstructed images to refine the caption. This process is performed iteratively, further progressively promoting the generation of more faithful and comprehensive descriptions. To mitigate the additional computational cost induced by the iterative process, we introduce RICO-Flash, which learns to generate captions like RICO using DPO. Extensive experiments demonstrate that our approach significantly improves caption accuracy and completeness, outperforms most baselines by approximately 10{\%} on both CapsBench and CompreCap."
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<abstract>Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimodal large language models (MLLMs) to enhance textual descriptions, but often suffer from inaccuracies due to hallucinations and incompleteness caused by missing fine-grained details. To address these limitations, we propose RICO, a novel framework that refines captions through visual reconstruction. Specifically, we leverage a text-to-image model to reconstruct a caption into a reference image, and prompt an MLLM to identify discrepancies between the original and reconstructed images to refine the caption. This process is performed iteratively, further progressively promoting the generation of more faithful and comprehensive descriptions. To mitigate the additional computational cost induced by the iterative process, we introduce RICO-Flash, which learns to generate captions like RICO using DPO. Extensive experiments demonstrate that our approach significantly improves caption accuracy and completeness, outperforms most baselines by approximately 10% on both CapsBench and CompreCap.</abstract>
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%0 Conference Proceedings
%T RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction
%A Wang, Yuchi
%A Cai, Yishuo
%A Ren, Shuhuai
%A Yang, Sihan
%A Yao, Linli
%A Liu, Yuanxin
%A Zhang, Yuanxing
%A Wan, Pengfei
%A Sun, Xu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-rico
%X Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimodal large language models (MLLMs) to enhance textual descriptions, but often suffer from inaccuracies due to hallucinations and incompleteness caused by missing fine-grained details. To address these limitations, we propose RICO, a novel framework that refines captions through visual reconstruction. Specifically, we leverage a text-to-image model to reconstruct a caption into a reference image, and prompt an MLLM to identify discrepancies between the original and reconstructed images to refine the caption. This process is performed iteratively, further progressively promoting the generation of more faithful and comprehensive descriptions. To mitigate the additional computational cost induced by the iterative process, we introduce RICO-Flash, which learns to generate captions like RICO using DPO. Extensive experiments demonstrate that our approach significantly improves caption accuracy and completeness, outperforms most baselines by approximately 10% on both CapsBench and CompreCap.
%U https://aclanthology.org/2025.emnlp-main.1105/
%P 21796-21815
Markdown (Informal)
[RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction](https://aclanthology.org/2025.emnlp-main.1105/) (Wang et al., EMNLP 2025)
ACL
- Yuchi Wang, Yishuo Cai, Shuhuai Ren, Sihan Yang, Linli Yao, Yuanxin Liu, Yuanxing Zhang, Pengfei Wan, and Xu Sun. 2025. RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21796–21815, Suzhou, China. Association for Computational Linguistics.