853 citations · 1.2k across the 11 of their papers we have counts for
9 papers · 1 filter
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
Corentin Dancette, Remi Cadene, Damien Teney +1
We introduce an evaluation methodology for visual question answering (VQA) to better diagnose cases of shortcut learning. These cases happen when a model exploits spurious statisti…
Overcoming Statistical Shortcuts for Open-ended Visual Counting
Corentin Dancette, Remi Cadene, Xinlei Chen +1
Machine learning models tend to over-rely on statistical shortcuts. These spurious correlations between parts of the input and the output labels does not hold in real-world setting…
RUBi: Reducing Unimodal Biases in Visual Question Answering
Remi Cadene, Corentin Dancette, Hedi Ben-younes +2
Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct answer without using the ima…
MUREL: Multimodal Relational Reasoning for Visual Question Answering
Remi Cadene, Hedi Ben-younes, Matthieu Cord +1
Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the vis…
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Hedi Ben-younes, Rémi Cadene, Nicolas Thome +1
Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combi…
Benchmark Analysis of Representative Deep Neural Network Architectures
Simone Bianco, Remi Cadene, Luigi Celona +1
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. For each DNN multiple performance…
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Hedi Ben-younes, Rémi Cadene, Matthieu Cord +1
Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between quest…
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification
Rémi Cadène, Thomas Robert, Nicolas Thome +1
Our approach is among the three best to tackle the M2CAI Workflow challenge. The latter consists in recognizing the operation phase for each frames of endoscopic videos. In this te…
Master's Thesis : Deep Learning for Visual Recognition
Rémi Cadène, Nicolas Thome, Matthieu Cord
The goal of our research is to develop methods advancing automatic visual recognition. In order to predict the unique or multiple labels associated to an image, we study different…