1 citations · 1 across the 4 of their papers we have counts for
10 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…
Neural Disparity Refinement for Arbitrary Resolution Stereo
Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez +4
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile ph…
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…
Distilled Semantics for Comprehensive Scene Understanding from Videos
Fabio Tosi, Filippo Aleotti, Pierluigi Zama Ramirez +4
Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) f…