2 citations · 4 across the 9 of their papers we have counts for
6 papers · 1 filter
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation
Marco Stefani, Edoardo Parpaiola, Matteo Bino +5
High-risk applications of deep learning require precise characterization of model decision-making. We conduct a systematic explainability evaluation across Convolutional Neural Net…
Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
Simone Mosco, Daniel Fusaro, Alberto Pretto
Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial…
DPGLA: Bridging the Gap between Synthetic and Real Data for Unsupervised Domain Adaptation in 3D LiDAR Semantic Segmentation
Wanmeng Li, Simone Mosco, Daniel Fusaro +1
Annotating real-world LiDAR point clouds for use in intelligent autonomous systems is costly. To overcome this limitation, self-training-based Unsupervised Domain Adaptation (UDA)…
Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios
Simone Mosco, Daniel Fusaro, Wanmeng Li +2
LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong perform…
Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds
Daniel Fusaro, Federico Magistri, Jens Behley +2
Accurate and consistent fruit monitoring over time is a key step toward automated agricultural production systems. However, this task is inherently difficult due to variations in f…
Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic Segmentation
Daniel Fusaro, Simone Mosco, Emanuele Menegatti +1
Semantic segmentation of point clouds is an essential task for understanding the environment in autonomous driving and robotics. Recent range-based works achieve real-time efficien…