Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image. To build an efficient VQA algorithm, a large amount of QA data is required which is very expensive. Generating synthetic QA pairs based on templates is a practical way to obtain data. However, VQA models trained on those data do not perform well on complex, human-written questions. To address this issue, we propose a new method called {\it chain of QA for human-written questions} (CoQAH). CoQAH utilizes a sequence of QA interactions between a large language model and a VQA model trained on synthetic data to reason and derive logical answers for human-written questions. We tested the effectiveness of CoQAH on two types of human-written VQA datasets for 3D-rendered and chest X-ray images and found that it achieved state-of-the-art accuracy in both types of data. Notably, CoQAH outperformed general vision-language models, VQA models, and medical foundation models with no finetuning.

翻译：视觉问答（VQA）是一项给定图像并提出一系列相关问题的任务。为构建高效VQA算法，需要大量问答数据，而这类数据获取成本极高。基于模板生成合成问答对是获取数据的实用方法。然而，在这些数据上训练的VQA模型难以有效处理复杂的人类撰写问题。针对该问题，我们提出一种名为"人类撰写问题问答链"（CoQAH）的新方法。CoQAH利用大语言模型与基于合成数据训练的VQA模型之间的序列化问答交互，对用户提出的问题进行推理并推导出逻辑答案。我们在两类人类撰写的VQA数据集（3D渲染图像与胸部X光图像）上验证了CoQAH的有效性，发现其在两类数据中均达到最优精度。值得注意的是，CoQAH在无需微调的情况下，性能超越了通用视觉语言模型、VQA模型及医学基础模型。