1 citations · 1 across the 4 of their papers we have counts for
4 papers
Self-Distillation for Unsupervised 3D Domain Adaptation
Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez +2
Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as…
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez +2
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align fe…
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation
Adriano Cardace, Luca De Luigi, Pierluigi Zama Ramirez +2
Although recent semantic segmentation methods have made remarkable progress, they still rely on large amounts of annotated training data, which are often infeasible to collect in t…
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries
Adriano Cardace, Pierluigi Zama Ramirez, Samuele Salti +1
Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performin…