5 citations · 5 across the 1 of their papers we have counts for
8 papers
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning
Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux +1
This paper proposes a step toward obtaining general models of knowledge for facial analysis, by addressing the question of multi-source transfer learning. More precisely, the propo…
MFAS: Multimodal Fusion Architecture Search
Juan-Manuel Pérez-Rúa, Valentin Vielzeuf, Stéphane Pateux +2
We tackle the problem of finding good architectures for multimodal classification problems. We propose a novel and generic search space that spans a large number of possible fusion…
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…