1 code implementation • 9 Jun 2022 • Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Bethune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin, Thomas Serre
Today's most advanced machine-learning models are hardly scrutable.
1 code implementation • CVPR 2019 • Remi Cadene, Hedi Ben-Younes, Matthieu Cord, Nicolas Thome
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
2 code implementations • 1 Oct 2018 • Simone Bianco, Remi Cadene, Luigi Celona, Paolo Napoletano
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.
1 code implementation • 24 Jun 2019 • Remi Cadene, Corentin Dancette, Hedi Ben-Younes, Matthieu Cord, Devi Parikh
We propose RUBi, a new learning strategy to reduce biases in any VQA model.
Ranked #7 on Visual Question Answering (VQA) on VQA-CP
1 code implementation • NeurIPS 2019 • Remi Cadene, Corentin Dancette, Hedi Ben Younes, Matthieu Cord, Devi Parikh
We propose RUBi, a new learning strategy to reduce biases in any VQA model.
1 code implementation • NeurIPS 2021 • Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre
We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices.
1 code implementation • ICCV 2021 • Corentin Dancette, Remi Cadene, Damien Teney, Matthieu Cord
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
1 code implementation • 17 Jun 2020 • Corentin Dancette, Remi Cadene, Xinlei Chen, Matthieu Cord
First, we propose the Modifying Count Distribution (MCD) protocol, which penalizes models that over-rely on statistical shortcuts.
1 code implementation • 6 Dec 2021 • Julien Colin, Thomas Fel, Remi Cadene, Thomas Serre
A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions.
no code implementations • 8 Aug 2021 • Mohit Vaishnav, Remi Cadene, Andrea Alamia, Drew Linsley, Rufin VanRullen, Thomas Serre
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.
no code implementations • CVPR 2023 • Thomas Fel, Melanie Ducoffe, David Vigouroux, Remi Cadene, Mikael Capelle, Claire Nicodeme, Thomas Serre
A variety of methods have been proposed to try to explain how deep neural networks make their decisions.