research
∙
06/11/2023
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
Feature visualization has gained substantial popularity, particularly af...
share
research
∙
06/11/2023
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
In recent years, concept-based approaches have emerged as some of the mo...
research
∙
11/17/2022
CRAFT: Concept Recursive Activation FacTorization for Explainability
Attribution methods are a popular class of explainability methods that u...
research
∙
06/09/2022
Xplique: A Deep Learning Explainability Toolbox
Today's most advanced machine-learning models are hardly scrutable. The ...
research
∙
02/15/2022
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 neura...
research
∙
12/06/2021
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
A multitude of explainability methods and theoretical evaluation scores ...
research
∙
11/07/2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
We describe a novel attribution method which is grounded in Sensitivity ...
research
∙
08/08/2021
Understanding the computational demands underlying visual reasoning
Visual understanding requires comprehending complex visual relations bet...
research
∙
04/07/2021
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
We introduce an evaluation methodology for visual question answering (VQ...
research
∙
06/17/2020
Overcoming Statistical Shortcuts for Open-ended Visual Counting
Machine learning models tend to over-rely on statistical shortcuts. Thes...
research
∙
06/24/2019
RUBi: Reducing Unimodal Biases in Visual Question Answering
Visual Question Answering (VQA) is the task of answering questions about...
research
∙
02/25/2019
MUREL: Multimodal Relational Reasoning for Visual Question Answering
Multimodal attentional networks are currently state-of-the-art models fo...
research
∙
01/31/2019
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Multimodal representation learning is gaining more and more interest wit...
research
∙
10/01/2018
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neur...
research
∙
05/02/2018
Images & Recipes: Retrieval in the cooking context
Recent advances in the machine learning community allowed different use ...
research
∙
04/30/2018
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Designing powerful tools that support cooking activities has rapidly gai...
research
∙
05/18/2017
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Bilinear models provide an appealing framework for mixing and merging in...
research
∙
10/18/2016
Master's Thesis : Deep Learning for Visual Recognition
The goal of our research is to develop methods advancing automatic visua...
research
∙
10/18/2016