2 papers
cs.CV2024
Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
Björn Michele, Alexandre Boulch, Tuan-Hung Vu +3
We tackle the challenging problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled tar…
cs.CV2024
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Björn Michele, Alexandre Boulch, Gilles Puy +3
Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case…