Sébastien Lefèvre is currently a Full Professor in Computer Science at the University of South Brittany (Vannes Institute of Technology) since September 2010. He founded the OBELIX group from IRISA laboratory, and led the group from 2013 to 2021 (Prof. Nicolas Courty is leading the group since March 2021). He is also coordinating the GeoData Science track within the Erasmus Mundus Copernicus Master in Digital Earth. His main research topics are image analysis/processing, pattern recognition and indexing, machine learning, deep learning and data mining with applications in remote sensing for Earth observation.
Deep Learning in Computer Vision: Are Numerous Labels the Holy Grail?
Deep Learning has been successful in a wide range of computer vision tasks, at the cost of high computational resources and large labeled datasets required to train the models. The latter is a strong bottleneck in numerous applications where collecting annotated data is challenging.
In this talk, I will present some of our works attempting to alleviate our need for large annotated datasets. More precisely, the methods we develop rely on semi-supervised, weakly-supervised, unsupervised settings, domain adaptation, data simulation, active learning, among other frameworks. Various applications in Earth Observation will be provided to illustrate the relevance of these solutions for a wide range of problems such as semantic segmentation, image classification, or object detection.
His talk takes place in Thursday, June 15, 2023 at 14:00 in G108.
Jiri Mekyska is head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology, where he leads a multidisciplinary team of researchers (signal processing engineers, data scientists, neuroscientists, psychologists) with a special focus on the development of new digital biomarkers facilitating understanding, diagnosis and monitoring of neurodegenerative (e.g. Parkinson’s disease) and neurodevelopmental (e.g. dysgraphia) disorders.
Acoustic analysis of speech and voice disorders in patients with Parkinson’s disease
Parkinson’s disease (PD) is the second most frequent neurodegenerative disease, which is associated with several motor and non-motor features. Up to 90 % of PD patients develop a motor speech disorder called hypokinetic dysarthria (HD). HD manifests in the field of phonation (e.g. increased instability of articulatory organs, microperturbation in pitch and amplitude), articulation (e.g. rigidity of tongue and jaw, slow alternating motion rate), prosody (e.g. monopitch, monoloudness), and respiration (e.g. airflow insufficiency). Acoustic analysis of these specific speech/voice disorders enables neurologists and speech-language therapists to effectively monitor the progress of PD as well as to diagnose it. In the frame of this talk, we will present a concept of acoustic HD analysis. Consequently, we will present some recent findings focused on the prediction of motor (freezing of gait) and non-motor (cognitive) deficits based on the acoustic analysis, we will discuss an application of acoustic HD analysis in treatment effect monitoring (based on high-frequency repetitive transcranial magnetic stimulation), and in PD diagnosis. Finally, we will present some future directions in terms of integration into Health 4.0 systems.
His talk takes place in Tuesday, May 16, 2023 at 15:00 in E105.