32 citations · 109 across the 15 of their papers we have counts for
6 papers · 1 filter
Multi-Level Sensor Fusion with Deep Learning
Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux +1
In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors. This approach is de…
The Many Moods of Emotion
Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux +1
This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuo…
CentralNet: a Multilayer Approach for Multimodal Fusion
Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux +1
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past mult…
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets
Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux +2
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drasti…
CAKE: Compact and Accurate K-dimensional representation of Emotion
Corentin Kervadec, Valentin Vielzeuf, Stéphane Pateux +2
Numerous models describing the human emotional states have been built by the psychology community. Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are…
Efficient Progressive Neural Architecture Search
Juan-Manuel Perez-Rua, Moez Baccouche, Stephane Pateux
This paper addresses the difficult problem of finding an optimal neural architecture design for a given image classification task. We propose a method that aggregates two main resu…