activity
20162023
most citedJoint Detection and Tracking in Videos with Identification Features

18 citations · 91 across the 28 of their papers we have counts for

collaborators
Showing 2022Show all

7 papers · 1 filter

cs.LG2022★ 1 cited

Are we certain it's anomalous?

Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal +4

The progress in modelling time series and, more generally, sequences of structured data has recently revamped research in anomaly detection. The task stands for identifying abnorma…

cs.CV2022

Query-Guided Networks for Few-shot Fine-grained Classification and Person Search

Bharti Munjal, Alessandro Flaborea, Sikandar Amin +2

Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both t…

cs.RO2022★ 2 cited

Pose Forecasting in Industrial Human-Robot Collaboration

Alessio Sampieri, Guido D'Amely, Andrea Avogaro +5

Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the…

cs.CV2022★ 1 cited

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…

cs.CV2022

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…

cs.CV2022★ 2 cited

Under the Hood of Transformer Networks for Trajectory Forecasting

Luca Franco, Leonardo Placidi, Francesco Giuliari +3

Transformer Networks have established themselves as the de-facto state-of-the-art for trajectory forecasting but there is currently no systematic study on their capability to model…

physics.geo-ph2022

Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress

Laura Laurenti, Elisa Tinti, Fabio Galasso +2

Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for…