Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions

Understand

Deep neural networks have been critical in the task of Visual Question Answering (VQA), with research traditionally focused on improving model accuracy.

• Recently, however, there has been a trend towards evaluating the robustness of these models against adversarial attacks.
• This involves assessing the accuracy of VQA models under increasing levels of noise in the input, which can target either the image or the proposed query question, dubbed the main question.
• However, there is currently a lack of proper analysis of this aspect of VQA.