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- Centre National de la Recherche ScientifiqueFR13 papers
- Institut national de recherche en sciences et technologies du numériqueFR10 papers
- Sorbonne UniversitéFR8 papers
- Laboratoire d'Informatique Gaspard-MongeFR7 papers
- Télécom ParisFR6 papers
- Centre de Robotique5 papers
- Institut de Recherche en Informatique et Systèmes AléatoiresFR5 papers
- Université Gustave EiffelFR5 papers
- Hôpital Saint-MichelFR4 papers
- Laboratoire d’Informatique et SystèmesFR4 papers
- Laboratoire Traitement et Communication de l’InformationFR4 papers
- Technicolor (France)FR4 papers
7 papers · 1 filter
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher +2
Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks,…
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Maximilian Jaritz, Raoul de Charette, Emilie Wirbel +2
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse dept…
End-to-End Race Driving with Deep Reinforcement Learning
Maximilian Jaritz, Raoul de Charette, Marin Toromanoff +2
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly pr…
End to End Vehicle Lateral Control Using a Single Fisheye Camera
Marin Toromanoff, Emilie Wirbel, Frédéric Wilhelm +3
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failur…
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
Waleed Ali, Sherif Abdelkarim, Mohamed Zahran +2
Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding enviro…
Finding beans in burgers: Deep semantic-visual embedding with localization
Martin Engilberge, Louis Chevallier, Patrick Pérez +1
Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic re…