no code implementations • 18 Dec 2023 • Luis Lugo, Valentin Vielzeuf
Self-supervised learning enables the training of large neural models without the need for large, labeled datasets.
no code implementations • 3 Nov 2023 • Lucas Druart, Valentin Vielzeuf, Yannick Estève
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs (a. k. a dialogue state tracking) is key to a smooth interaction.
no code implementations • 3 Nov 2023 • Lucas Druart, Léo Jacqmin, Benoît Favre, Lina Maria Rojas-Barahona, Valentin Vielzeuf
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction.
1 code implementation • 20 Apr 2023 • Léo Jacqmin, Lucas Druart, Yannick Estève, Benoît Favre, Lina Maria Rojas-Barahona, Valentin Vielzeuf
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language. In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 9 Dec 2021 • Valentin Vielzeuf, Grigory Antipov
The Automated Speech Recognition (ASR) community experiences a major turning point with the rise of the fully-neural (End-to-End, E2E) approaches.
1 code implementation • 31 Aug 2021 • Maxime Burchi, Valentin Vielzeuf
The recently proposed Conformer architecture has shown state-of-the-art performances in Automatic Speech Recognition by combining convolution with attention to model both local and global dependencies.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
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.
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 • 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 • 31 Oct 2018 • Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Frédéric Jurie
This paper presents a novel approach to the facial expression generation problem.
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 • 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.
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 #24 on Facial Expression Recognition (FER) on AffectNet (Accuracy (7 emotion) metric)
no code implementations • 21 Sep 2017 • Valentin Vielzeuf, Stéphane Pateux, Frédéric Jurie
This paper addresses the question of emotion classification.