Search Results for author:
Found 10 papers, 3 papers with code
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos
In this work, we propose to directly compute this ordered list by sparsely browsing the video and selecting one frame per action instance, task known as action spotting in literature.
Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigation
Transferring learning-based models to the real world remains one of the hardest problems in model-free control theory.
Interactive Learning for Semantic Segmentation in Earth Observation
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding.
SALAD: Self-Assessment Learning for Action Detection
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.
Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust
We compare it, in realistic simulations, to a model-free controller that uses the same deep reinforcement learning framework for the control of a micro aerial vehicle under wind gust.
Demotivate adversarial defense in remote sensing
Convolutional neural networks are currently the state-of-the-art algorithms for many remote sensing applications such as semantic segmentation or object detection.
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing
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
Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing.
Carpet-bombing patch: attacking a deep network without usual requirements
Although deep networks have shown vulnerability to evasion attacks, such attacks have usually unrealistic requirements.
Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround Airfoil Geometries
Physical based simulations can be very time and computationally demanding tasks.
