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
A Simple Recipe for Language-guided Domain Generalized Segmentation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette
Computer Vision and Pattern Recognition (CVPR), 2024
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Bjoern Michele, Alexandre Boulch, Gilles Puy, Tuan-Hung Vu, Renaud Marlet and Nicolas Courty
International Conference on 3D Vision (3DV), 2024
PØDA: Prompt-driven Zero-shot Domain Adaptation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette
International Conference on Computer Vision (ICCV), 2023
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
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
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
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