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 Yann CABANES: Please contact me for any information or if you want to present your work to our team.
Previous seminars
2019 / 2019-20 / 2020-21 / 2021-22 / 2022-23 / 2023-24 / 2024-25
Upcoming seminars (2025-26)
- Date: Wednesday, September 10, 2025 between 9 a.m. and 12 p.m.
- Room: D117
- Speaker: Zhenghang Yuan
- Title: Vision-Language Understanding in Remote Sensing
- Abstract: Advances in remote sensing have greatly expanded Earth observation capabilities, yet the complexity of such data remains challenging for non-expert users. Natural language offers a powerful medium to bridge this gap. In this talk, I will explore key questions in vision-language understanding for remote sensing: (1) How can we design effective learning strategies for visual question answering (RSVQA)? (2) How can RSVQA be extended with change detection to capture temporal dynamics? (3) How can we achieve more fine-grained and interpretable outputs, such as pixel-level visual explanations? (4) How can recent progress in large language models (LLMs) enable the creation of large-scale, high-quality image–text datasets?
- Date: Wednesday, September 10, 2025 between 9 a.m. and 12 p.m.
- Room: D117
- Speaker: Yuanyuan Wang
- Title: Uncertainty quantification in machine learning for Earth observation
- Abstract: Machine learning is widely applied in Earth observation to derive geoinformation including critical products like biomass estimates, informing vital decisions for humanity. However, many ML models remain as black boxes, necessitating methods to quantify prediction uncertainties. I will explain the cause of the uncertainty in machine learning and explore uncertainty quantification techniques, such as Bayesian methods and ensemble approaches, as well as demonstration of uncertainty quantification in different machine learning tasks in EO.