Jean-Marc Odobez

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Publications associées (77)

Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

Achieving robust multi-person 2D body landmark localization and pose estimation is essential for human behavior and interaction understanding as encountered for instance in HRI settings. Accurate methods have been proposed recently, but they usually rely o ...

2020

Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis

Jean-Marc Odobez, Yu Yu, Gang Liu

As an indicator of human attention gaze is a subtle behavioral cue which can be exploited in many applications. However, inferring 3D gaze direction is challenging even for deep neural networks given the lack of large amount of data (groundtruthing gaze is ...

IEEE2019

Improving speech embedding using crossmodal transfer learning with audio-visual data

Jean-Marc Odobez

Learning a discriminative voice embedding allows speaker turns to be compared directly and efficiently, which is crucial for tasks such as diarization and verification. This paper investigates several transfer learning approaches to improve a voice embeddi ...

2019

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

Daniel Gatica-Perez, Jean-Marc Odobez, Gülcan Can

Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this ...

2018

Facing Employers and Customers: What Do Gaze and Expressions Tell About Soft Skills?

Daniel Gatica-Perez, Skanda Muralidhar, Jean-Marc Odobez, Rémy Alain Siegfried

Eye gaze and facial expressions are central to face-to-face social interactions. These behavioral cues and their connections to first impressions have been widely studied in psychology and computing literature, but limited to a single situation. Utilizing ...

ASSOC COMPUTING MACHINERY2018

Real-time Convolutional Networks for Depth-based Human Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We propose to combine recent Convolutional Neural Networks (CNN) models with depth imaging to obtain a reliable and fast multi-person pose estimation algorithm applicable to Human Robot Interaction (HRI) scenarios. Our hypothesis is that depth images conta ...

IEEE2018

Robust and Discriminative Speaker Embedding via Intra-Class Distance Variance Regularization

Jean-Marc Odobez

Learning a good speaker embedding is critical for many speech processing tasks, including recognition, verification, and diarization. To this end, we propose a complementary optimizing goal called intra-class loss to improve deep speaker embed dings learne ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Deep Neural Networks for Multiple Speaker Detection and Localization

Jean-Marc Odobez, Petr Motlicek, Weipeng He

We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require few ...

2018

Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network

Jean-Marc Odobez, Petr Motlicek, Weipeng He

We propose a novel multi-task neural network-based approach for joint sound source localization and speech/non-speech classification in noisy environments. The network takes raw short time Fourier transform as input and outputs the likelihood values for th ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We propose a deep-learning approach for people detection on depth imagery. The approach is designed to be deployed as an autonomous appliance for identifying people attacks and intrusion in video surveillance scenarios. To this end, we propose a fully-conv ...

IEEE2018

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