5 citations · 13 across the 4 of their papers we have counts for
5 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.…
Weakly-supervised continual learning for class-incremental segmentation
Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari +1
Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing. Starting from a neural network already trained for semantic…
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…
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…
DISIR: Deep Image Segmentation with Interactive Refinement
Gaston Lenczner, Bertrand Le Saux, Nicola Luminari +2
This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annota…