Assessing the Robustness of Visual Question Answering Models
This work addresses the lack of proper robustness analysis in VQA, which is important for researchers and practitioners in AI and computer vision, though it is incremental as it builds on existing trends in adversarial evaluation.
The paper tackles the problem of assessing the robustness of Visual Question Answering (VQA) models against adversarial attacks by introducing a method that uses semantically related questions as noise, proposing a robustness measure and datasets, and demonstrating its effectiveness in analyzing model robustness.
Deep neural networks have been playing an essential role in the task of Visual Question Answering (VQA). Until recently, their accuracy has been the main focus of research. Now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating the accuracy of these models under increasing levels of noisiness in the inputs of VQA models. In VQA, the attack can target the image and/or the proposed query question, dubbed main question, and yet there is a lack of proper analysis of this aspect of VQA. In this work, we propose a new method that uses semantically related questions, dubbed basic questions, acting as noise to evaluate the robustness of VQA models. We hypothesize that as the similarity of a basic question to the main question decreases, the level of noise increases. To generate a reasonable noise level for a given main question, we rank a pool of basic questions based on their similarity with this main question. We cast this ranking problem as a LASSO optimization problem. We also propose a novel robustness measure Rscore and two large-scale basic question datasets in order to standardize robustness analysis of VQA models. The experimental results demonstrate that the proposed evaluation method is able to effectively analyze the robustness of VQA models. To foster the VQA research, we will publish our proposed datasets.