Visual question answering (VQA) is a challenging task that requires the
ability to comprehend and reason with visual information. While recent
vision-language models have made strides, they continue to struggle with
zero-shot VQA, particularly in handling complex compositional questions and
adapting to new domains i.e. knowledge-based reasoning. This paper explores the
use of various prompting strategies, focusing on the BLIP2 model, to enhance
zero-shot VQA performance. We conduct a comprehensive investigation across
several VQA datasets, examining the effectiveness of different question
templates, the role of few-shot exemplars, the impact of chain-of-thought (CoT)
reasoning, and the benefits of incorporating image captions as additional
visual cues. Despite the varied outcomes, our findings demonstrate that
carefully designed question templates and the integration of additional visual
cues, like image captions, can contribute to improved VQA performance,
especially when used in conjunction with few-shot examples. However, we also
identify a limitation in the use of chain-of-thought rationalization, which
negatively affects VQA accuracy. Our study thus provides critical insights into
the potential of prompting for improving zero-shot VQA performance.

本研究探索了使用各种提示策略来增强零样本视觉问答性能的方法，重点关注 BLIP2 模型，通过在多个视觉问答数据集上进行全面研究，发现精心设计的问题模板和集成附加视觉提示，如图像标题，可以提高 VQA 绩效，特别是在与少量样本示例结合使用时。