37 citations · 40 across the 5 of their papers we have counts for
5 papers · 1 filter
High-resolution open-vocabulary object 6D pose estimation
Jaime Corsetti, Davide Boscaini, Francesco Giuliari +3
The generalisation to unseen objects in the 6D pose estimation task is very challenging. While Vision-Language Models (VLMs) enable using natural language descriptions to support 6…
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection
Mohamed L. Mekhalfi, Davide Boscaini, Fabio Poiesi
Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data. In this paper, we p…
PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding
Davide Boscaini, Fabio Poiesi
The recent trend in deep learning methods for 3D point cloud understanding is to propose increasingly sophisticated architectures either to better capture 3D geometries or by intro…
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci +3
Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particu…
Shape-from-intrinsic operator
Davide Boscaini, Davide Eynard, Michael M. Bronstein
Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observa…