Recent advances in Vision-Language Models (VLMs) and the scarcity of high-quality multi-modal alignment data have inspired numerous researches on synthetic VLM data generation. The conventional norm in VLM data construction uses a mixture of specialists in caption and OCR, or stronger VLM APIs and expensive human annotation. In this paper, we present World to Code (W2C), a meticulously curated multi-modal data construction pipeline that organizes the final generation output into a Python code format. The pipeline leverages the VLM itself to extract cross-modal information via different prompts and filter the generated outputs again via a consistency filtering strategy. Experiments have demonstrated the high quality of W2C by improving various existing visual question answering and visual grounding benchmarks across different VLMs. Further analysis also demonstrates that the new code parsing ability of VLMs presents better cross-modal equivalence than the commonly used detail caption ability. Our code is available at https://github.com/foundation-multimodal-models/World2Code.

本研究针对传统多模态数据生成中高质量对齐数据缺乏的问题，提出了一种名为“从世界到代码”（W2C）的新型多模态数据构建管道。该方法通过利用视觉语言模型（VLM）提取跨模态信息，并运用一致性过滤策略提高生成质量，实验表明其在视觉问答和视觉定位等基准任务上表现出色，展现了VLM在代码解析能力上的优势。

从世界到代码：通过自我指导的组合式标题生成和过滤实现多模态数据生成