Maximilian Jaritz — accepted AI-conference papers

2020

xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, Patrick Perez

CVPR 2020poster

Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of…

Cited by 224 PDF ↗code ↗Scholar ↗

2018

End-to-End Race Driving with Deep Reinforcement Learning

Maximilian Jaritz, Raoul de Charette, Marin Toromanoff, Etienne Perot, Fawzi Nashashibi

ICRA 2018poster

We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster convergence and more robust driving using only RGB image f…

Cited by 231 Source ↗Scholar ↗