Over the past few years, advances in commercial 3D sensors have substantially promoted the research of dynamic hand gesture recognition. On a other side, whole body gestures recognition has also attracted increasing attention since the emergence of Kinect like sensors. One may notice that both research topics deal with human-made motions and are li...
Présentation lors du colloque conclusif
This paper addresses the question of emotion classification. The task consists in predicting emotion labels (taken among a set of possible labels) best describing the emotions contained in short video clips. Building on a standard framework – lying in describing videos by audio and visual features used by a supervised classifier to infer the labels...
International audience
International audience
Evaluating potential musculoskeletal disorders risks in real workstations is challenging as the environment is cluttered, which makes it difficult to accurately assess workers' postures. Being marker-free and calibration-free, Microsoft Kinect is a promising device although it may be sensitive to occlusions. We propose and evaluate a RULA ergonomic...
Textures are homogeneous visual phenomena commonly appearing in the visual scene. They are usually characterized by randomness with some stationar-ity. They have been well studied in different domains, such as neuroscience, vision science and computer vision, and showed an excellent performance in many applications for machine intelligence. This bo...
International audience
Published in Multimedia Tools and Applications
We describe the “Multimodal Person Discovery in Broadcast TV” task of MediaEval 2015 benchmarking initiative. Participants were asked to return the names of people who can be both seen as well as heard in every shot of a collection of videos. The list of people was not known a priori and their names had to be discovered in an unsupervised way from ...
A large portion of data mining and analytic services use modern machine learning techniques, such as deep learning. The state-of-the-art results by deep learning come at the price of an intensive use of computing resources. The leading frameworks (e.g., TensorFlow) are executed on GPUs or on high-end servers in datacenters. On the other end, there ...
