RICO: Improving Accuracy and Completeness in Image Recaptioning via
  Visual Reconstruction
Authors: name: Yuchi Wang, name: Yishuo Cai, name: Shuhuai Ren, name: Sihan Yang, name: Linli Yao, name: Yuanxin Liu, name: Yuanxing Zhang, name: Pengfei Wan, name: Xu Sun

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. Code released at
https://github.com/wangyuchi369/RICO.