Seminar

The seminar of OBELIX team is currently held on thursdays 11:30 am, every two weeks, at the IRISA lab, Tohannic campus (bat. ENSIBS). Usually, the presentation lasts 30 min and is followed by a discussion with the team.

The seminar is coordinated by Chloé Friguet.

Previous seminars

2019 / 2019-20 / 2020-21 / 2021-22

Upcoming seminars (2021-2022)


  • Date:  March, 23th (wednesday)
  • Time: 14h00
  • Room : Paris (ONERA) and Visio
  • Speaker: Javiera CASTILLO ( PhD student, ONERA + IRISA) PhD Defense
  • Title:  Semi-supervised learning for large-scale Earth observation data understanding.
  • Abstract: Earth observation (EO) plays a significant role in the way we understand our planet and its dynamics. While plenty of data are available, they cannot be processed by humans only, so artificial intelligence has emerged as a solution to achieve automatic analysis of EO imagery. Still, most data are not exploited because they are unlabeled. Hence, algorithms beyond supervised learning are needed to get complete insight.
    This thesis investigates deep semi-supervised learning (SSL) for classification and segmentation in order to achieve EO data understanding at a large scale. First, we explore the potential of unlabeled data and propose tools for analyzing data representativeness for multi-location datasets. Then, we explore two ways of approaching the SSL problem. By discriminative modeling, first, we develop multi-task networks and auxiliary tasks to tackle semi-supervised semantic segmentation; second, we explore consistency regularization methods (e.g., FixMatch) to perform scene classification in EO data. Moving to generative modeling, we show the potential of joint energy-based models for semi-supervised classification and many other EO applications.
    Through extensive experiments, we show that SSL allows us to train algorithms with better performances and generalization capacities for land use and land cover mapping.
    Finally, our contributions also include the release of MiniFrance, the first dataset and open benchmark designed to assess and help design SSL in remote sensing, and part of the IEEE GRSS Data Fusion Contest 2022.

  • Date:  March, 31st
  • Time: 11:30
  • Room : D-001 (bat ENSIBS)
  • Speaker: Adam Herout (Brno University of Technology, Department of Computer Graphics and Multimedia, Rep. Tcheque)
  • Title:  Self-Supervised Learning for Sports Pose Recognition/Classification 
  • Abstract: We intend to help athletes with correct practice in sports poses. For that, we are developing algorithms of efficient/cheap classification of sports poses in real time on mobile devices. The poses should be learned in a few-shot manner. This leads to exploration of self-supervised learning methods and specific datasets and their processing. The talk might be interesting not only for someone interested in sports data processing, but for anyone interested in deep learning, few-shot learning and self-supervised learning.

  • Date:  May, 12th
  • Time: 14h00
  • Room :
  • Speaker: Laetitia CHAPEL (IRISA-OBELIX) HDR Defense
  • Title:  Machine learning for structured data
  • Abstract:

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