18 citations · 20 across the 5 of their papers we have counts for
5 papers
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
Davide Alessandro Coccomini, Roberto Caldelli, Claudio Gennaro +3
Deepfake detectors are typically trained on large sets of pristine and generated images, resulting in limited generalization capacity; they excel at identifying deepfakes created t…
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3
Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in vary…
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
Renato Sortino, Daniel Magro, Giuseppe Fiameni +9
In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being e…
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3
3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to impro…
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière +5
3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when…