no code implementations • 21 Sep 2017 • Valentin Vielzeuf, Stéphane Pateux, Frédéric Jurie
This paper addresses the question of emotion classification.
no code implementations • 30 Jul 2018 • Corentin Kervadec, Valentin Vielzeuf, Stéphane Pateux, Alexis Lechervy, Frédéric Jurie
Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks. Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e. g. anger, happiness, sadness, etc.)
Ranked #29 on Facial Expression Recognition (FER) on AffectNet
no code implementations • 8 Aug 2018 • Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Alexis Lechervy, Frédéric Jurie
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition.
2 code implementations • 22 Aug 2018 • Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux, Frédéric Jurie
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media.
no code implementations • 31 Oct 2018 • Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Frédéric Jurie
This paper presents a novel approach to the facial expression generation problem.
no code implementations • 5 Nov 2018 • Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux, Frédéric Jurie
In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors.
no code implementations • 15 Mar 2019 • Juan-Manuel Pérez-Rúa, Valentin Vielzeuf, Stéphane Pateux, Moez Baccouche, Frédéric Jurie
We tackle the problem of finding good architectures for multimodal classification problems.
no code implementations • 8 Nov 2019 • Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux, Frédéric Jurie
This model outperforms its teacher on novel tasks, achieving results on par with state-of-the-art methods on 15 facial analysis tasks (and domains), at an affordable training cost.
1 code implementation • 27 Jan 2020 • Yuqing Hu, Vincent Gripon, Stéphane Pateux
In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples.
6 code implementations • 6 Jun 2020 • Yuqing Hu, Vincent Gripon, Stéphane Pateux
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
1 code implementation • 18 Oct 2021 • Yuqing Hu, Vincent Gripon, Stéphane Pateux
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
2 code implementations • 24 Jan 2022 • Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stéphane Pateux, Vincent Gripon
Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available.
Ranked #1 on Few-Shot Learning on Mini-Imagenet 5-way (1-shot)
no code implementations • 18 Sep 2022 • Yuqing Hu, Stéphane Pateux, Vincent Gripon
Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot.