A Novel Framework for Robustness Analysis of Visual QA Models

Understand

Deep neural networks have been playing an essential role in many computer vision tasks including Visual Question Answering (VQA).

• Until recently, the study of their accuracy was the main focus of research but now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating their tolerance to varying noise levels.
• In VQA, adversarial attacks can target the image and/or the proposed main question and yet there is a lack of proper analysis of the later.
• In this work, we propose a flexible framework that focuses on the language part of VQA that uses semantically relevant questions, dubbed basic questions, acting as controllable noise to evaluate the robustness of VQA models.