  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.
