Assessing the Robustness of Visual Question Answering

by   Jia-Hong Huang, et al.

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.


A Novel Framework for Robustness Analysis of Visual QA Models

Deep neural networks have been playing an essential role in many compute...

Robustness Analysis of Visual QA Models by Basic Questions

Visual Question Answering (VQA) models should have both high robustness ...

Exploring Weaknesses of VQA Models through Attribution Driven Insights

Deep Neural Networks have been successfully used for the task of Visual ...

Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking

On the way towards general Visual Question Answering (VQA) systems that ...

Learning from Lexical Perturbations for Consistent Visual Question Answering

Existing Visual Question Answering (VQA) models are often fragile and se...

Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?

To be reliable on rare events is an important requirement for systems ba...

An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games

Guessing games are a prototypical instance of the "learning by interacti...