Generalist visual captioning goes beyond a simple appearance description task, but requires integrating a series of visual cues into a caption and handling various visual domains. In this task, current open-source models present a large performance gap with commercial ones, which limits various applications such as data synthesis. To bridge the gap, this paper proposes CapFlow, a novel multi-agent collaboration workflow. CapFlow demonstrates for the first time that, by capitalizing on open-source models, it is possible to achieve caption quality on par with GPT-4.1 in various domains with an 89.5% reduction in costs. By leveraging CapFlow as the data synthesizer, we produce high-quality visual captions from image and video domains at scale, and obtain a generalist visual captioner via fine-tuning, namely MetaCaptioner. Through extensive experiments, we show that MetaCaptioner not only achieves comparable captioning capabilities with commercial models but also reaches top-tier multimodal performance in the open-source community. We hope CapFlow and MetaCaptioner can benefit future multimodal research by providing a strong and cost-effective visual captioning solution.
@article{arxiv.2510.12126,
title = {MetaCaptioner: Towards Generalist Visual Captioning with Open-source Suites},
author = {Zhenxin Lei and Zhangwei Gao and Changyao Tian and Erfei Cui and Guanzhou Chen and Danni Yang and Yuchen Duan and Zhaokai Wang and Wenhao Li and Weiyun Wang and Xiangyu Zhao and Jiayi Ji and Yu Qiao and Wenhai Wang and Gen Luo},
journal= {arXiv preprint arXiv:2510.12126},
year = {2025}
}