The zero-shot performance of visual question answering (VQA) models relies
heavily on prompts. For example, a zero-shot VQA for disaster scenarios could
leverage well-designed Chain of Thought (CoT) prompts to stimulate the model's
potential. However, using CoT prompts has some problems, such as causing an
incorrect answer in the end due to the hallucination in the thought process. In
this paper, we propose a zero-shot VQA named Flood Disaster VQA with Two-Stage
Prompt (VQA-TSP). The model generates the thought process in the first stage
and then uses the thought process to generate the final answer in the second
stage. In particular, visual context is added in the second stage to relieve
the hallucination problem that exists in the thought process. Experimental
results show that our method exceeds the performance of state-of-the-art
zero-shot VQA models for flood disaster scenarios in total. Our study provides
a research basis for improving the performance of CoT-based zero-shot VQA.

本论文提出了一种名为洪水灾害 VQA-TSP 的零样本视觉问答模型，通过两阶段提示生成思考过程并使用视觉上下文来减轻思考过程中的幻觉问题，实验结果显示我们的方法在洪水灾害场景中超过了现有零样本 VQA 模型的性能，为改进基于 CoT 的零样本 VQA 性能提供了研究基础。