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Found 11 papers, 9 papers with code
Xplique: A Deep Learning Explainability Toolbox
Today's most advanced machine-learning models are hardly scrutable.
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
A variety of methods have been proposed to try to explain how deep neural networks make their decisions.
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions.
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices.
Understanding the computational demands underlying visual reasoning
Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different vs. spatial-relation judgments) and the number of relations used to compose the underlying rules.
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
We use this new evaluation in a large-scale study of existing approaches for VQA.
Ranked #1 on
Visual Question Answering (VQA)
on VQA-CE
Overcoming Statistical Shortcuts for Open-ended Visual Counting
First, we propose the Modifying Count Distribution (MCD) protocol, which penalizes models that over-rely on statistical shortcuts.
RUBi: Reducing Unimodal Biases for Visual Question Answering
We propose RUBi, a new learning strategy to reduce biases in any VQA model.
RUBi: Reducing Unimodal Biases in Visual Question Answering
We propose RUBi, a new learning strategy to reduce biases in any VQA model.
Ranked #7 on
Visual Question Answering (VQA)
on VQA-CP
MUREL: Multimodal Relational Reasoning for Visual Question Answering
In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images.
Ranked #1 on
Visual Question Answering (VQA)
on TDIUC
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition.
