Domain adaptation

Deep learning and reinforcement learning are key technologies for autonomous driving. One of the challenges they face is to adapt to conditions which differ from those met during training. To improve systems’ performance in such situations, we explore so-called “domain adaptation” techniques, as in AdvEnt at CVPR’19 and DADA its extension at ICCV’19. We propose new solutions to more practical DA scenarios in MTAF (ICCV'21) to handle multiple target domains and in BUDA (CVIU'21) to handle new target classes. In xMUDA (CVPR'20), we introduce a new framework to tackle the challenging adaptation problem on both 2D image and 3D point-cloud spaces.

Publications

Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Antoine Saporta, Arthur Douillard, Tuan-Hung Vu, Patrick Pérez and Matthieu Cord
Computer Vision and Pattern Recognition (CVPR) Workshop, 2022


Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, and Patrick Pérez
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022


Handling new target classes in semantic segmentation with domain adaptation

Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez
Computer Vision and Image Understanding (CVIU), 2021


Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

Antoine Saporta, Tuan-Hung Vu, Matthieu Cord and Patrick Pérez
International Conference on Computer Vision (ICCV), 2021


Semantic Palette: Guiding Scene Generation with Class Proportions

Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain, Patrick Pérez and Matthieu Cord
Computer Vision and Pattern Recognition (CVPR), 2021


Confidence Estimation via Auxiliary Models

Charles Corbière, Nicolas Thome, Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021


ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation

Antoine Saporta, Tuan-Hung Vu, Matthieu Cord and Patrick Pérez
Computer Vision and Pattern Recognition Workshop on Scalability in Autonomous Driving, 2020


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

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, and Patrick Pérez
Computer Vision and Pattern Recognition (CVPR), 2020


DADA: Depth-aware Domain Adaptation in Semantic Segmentation

Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez
International Conference on Computer Vision (ICCV), 2019


AdvEnt: Adversarial Entropy minimization for domain adaptation in semantic segmentation

Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez
Computer Vision and Pattern Recognition (CVPR), 2019