5 citations · 8 across the 2 of their papers we have counts for
8 papers
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing
Gaston Lenczner, Adrien Chan-Hon-Tong, Bertrand Le Saux +2
We propose in this article to build up a collaboration between a deep neural network and a human in the loop to swiftly obtain accurate segmentation maps of remote sensing images.…
Demotivate adversarial defense in remote sensing
Adrien Chan-Hon-Tong, Gaston Lenczner, Aurelien Plyer
Convolutional neural networks are currently the state-of-the-art algorithms for many remote sensing applications such as semantic segmentation or object detection. However, these a…
Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust
Thomas Chaffre, Julien Moras, Adrien Chan-Hon-Tong +4
Navigation problems under unknown varying conditions are among the most important and well-studied problems in the control field. Classic model-based adaptive control methods can b…
SALAD: Self-Assessment Learning for Action Detection
Guillaume Vaudaux-Ruth, Adrien Chan-Hon-Tong, Catherine Achard
Literature on self-assessment in machine learning mainly focuses on the production of well-calibrated algorithms through consensus frameworks i.e. calibration is seen as a problem.…
Interactive Learning for Semantic Segmentation in Earth Observation
Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari +2
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding. However, these maps are often partially inaccurate due to a v…
Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigation
Thomas Chaffre, Julien Moras, Adrien Chan-Hon-Tong +1
Transferring learning-based models to the real world remains one of the hardest problems in model-free control theory. Due to the cost of data collection on a real robot and the li…