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Found 11 papers, 2 papers with code
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions
This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models.
Network Moments: Extensions and Sparse-Smooth Attacks
This has incited research on the reaction of DNNs to noisy input, namely developing adversarial input attacks and strategies that lead to robust DNNs to these attacks.
Analytical Moment Regularizer for Training Robust Networks
In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input.
Assessing the Robustness of Visual Question Answering Models
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.
Expected Tight Bounds for Robust Deep Neural Network Training
al. that bounded input intervals can be inexpensively propagated from layer to layer through deep networks.
Expected Tight Bounds for Robust Training
In this paper, we closely examine the bounds of a block of layers composed in the form of Affine-ReLU-Affine.
Analytical Moment Regularizer for Gaussian Robust Networks
Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours.
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input
Moreover, we show how these expressions can be used to systematically construct targeted and non-targeted adversarial attacks.
A Novel Framework for Robustness Analysis of Visual QA Models
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.
Robustness Analysis of Visual QA Models by Basic Questions
Visual Question Answering (VQA) models should have both high robustness and accuracy.
VQABQ: Visual Question Answering by Basic Questions
Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question.
