Visual question answering (VQA) is a fundamental and essential AI task, and VQA-based disaster scenario understanding is a hot research topic. For instance, we can ask questions about a disaster image by the VQA model and the answer can help identify whether anyone or anything is affected by the disaster. However, previous VQA models for disaster damage assessment have some shortcomings, such as limited candidate answer space, monotonous question types, and limited answering capability of existing models. In this paper, we propose a zero-shot VQA model named Zero-shot VQA for Flood Disaster Damage Assessment (ZFDDA). It is a VQA model for damage assessment without pre-training. Also, with flood disaster as the main research object, we build a Freestyle Flood Disaster Image Question Answering dataset (FFD-IQA) to evaluate our VQA model. This new dataset expands the question types to include free-form, multiple-choice, and yes-no questions. At the same time, we expand the size of the previous dataset to contain a total of 2,058 images and 22,422 question-meta ground truth pairs. Most importantly, our model uses well-designed chain of thought (CoT) demonstrations to unlock the potential of the large language model, allowing zero-shot VQA to show better performance in disaster scenarios. The experimental results show that the accuracy in answering complex questions is greatly improved with CoT prompts. Our study provides a research basis for subsequent research of VQA for other disaster scenarios.

翻译：视觉问答（VQA）是人工智能领域一项基础且关键的任务，基于VQA的灾害场景理解是当前研究热点。例如，通过VQA模型对灾害图像进行提问，其答案可帮助判断人员或财产是否受灾害影响。然而，现有用于灾害评估的VQA模型存在候选答案空间受限、问题类型单一、已有模型回答能力不足等问题。本文提出一种名为“零样本洪灾损害评估VQA模型”（ZFDDA）的零样本VQA模型，该模型无需预训练即可进行灾害损伤评估。同时，以洪灾为主要研究对象，我们构建了“自由式洪灾图像问答数据集”（FFD-IQA）以评估模型性能。该新数据集扩展了问题类型，包含自由形式、多项选择和是非判断题。此外，我们将先前数据集规模扩充至包含2,058张图像和22,422组问题-元答案对。尤为关键的是，本模型利用精心设计的思维链（CoT）演示，充分释放大语言模型的潜力，使零样本VQA在灾害场景中展现出更优性能。实验结果表明，基于CoT提示的复杂问题回答准确率显著提升。本研究为后续面向其他灾害场景的VQA研究奠定了基础。