@inproceedings{jia-etal-2026-re3cap,
title = "{R}e$^3${C}ap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning",
author = "Jia, Haonan and
Dong, Shichao and
Sun, Zenghui and
Zheng, Jiawen and
Miao, Ziqi and
Shi, Gege and
Zhao, Qiuyu and
Lan, Jinsong and
Zhu, Xiaoyong and
Zheng, Bo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2117/",
pages = "48154--48169",
ISBN = "979-8-89176-459-0",
abstract = "Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies. This limitation leads to a performance gap between RL and Supervised Fine-Tuning (SFT). In this paper, we argue that multi-modal retrieval can serve as an effective reasoning signal for caption refinement. Based on this insight, we present the Retrieval-Guided Refinement for Image Captioning (Re$^3$Cap), a retrieval-guided reasoning strategy that enhances image captioning by performing retrieval on support sets. Instantiated by Caption Refinement Suggester (CRS) and Caption Quality Assessor (CQA), this strategy identifies hallucinations and omissions in image captions, leading to more accurate and detailed descriptions. Extensive experiments demonstrate the superiority of our method in image captioning, even compared with Supervised Fine-Tuning. Especially, Re$^3$Cap outperforms GRPO with an average improvement of 8.64{\%} in relation reasoning on the COCO-LN500 benchmark."
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%0 Conference Proceedings
%T Re³Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning
%A Jia, Haonan
%A Dong, Shichao
%A Sun, Zenghui
%A Zheng, Jiawen
%A Miao, Ziqi
%A Shi, Gege
%A Zhao, Qiuyu
%A Lan, Jinsong
%A Zhu, Xiaoyong
%A Zheng, Bo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jia-etal-2026-re3cap
%X Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies. This limitation leads to a performance gap between RL and Supervised Fine-Tuning (SFT). In this paper, we argue that multi-modal retrieval can serve as an effective reasoning signal for caption refinement. Based on this insight, we present the Retrieval-Guided Refinement for Image Captioning (Re³Cap), a retrieval-guided reasoning strategy that enhances image captioning by performing retrieval on support sets. Instantiated by Caption Refinement Suggester (CRS) and Caption Quality Assessor (CQA), this strategy identifies hallucinations and omissions in image captions, leading to more accurate and detailed descriptions. Extensive experiments demonstrate the superiority of our method in image captioning, even compared with Supervised Fine-Tuning. Especially, Re³Cap outperforms GRPO with an average improvement of 8.64% in relation reasoning on the COCO-LN500 benchmark.
%U https://aclanthology.org/2026.emnlp-main.2117/
%P 48154-48169
Markdown (Informal)
[Re3Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning](https://aclanthology.org/2026.emnlp-main.2117/) (Jia et al., EMNLP 2026)
ACL
- Haonan Jia, Shichao Dong, Zenghui Sun, Jiawen Zheng, Ziqi Miao, Gege Shi, Qiuyu Zhao, Jinsong Lan, Xiaoyong Zhu, and Bo Zheng. 2026. Re3Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 48154–48169, Budapest, Hungary. Association for Computational Linguistics.