activity
20172025
most citedA Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces

27 citations · 29 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CV2025

Spike-TBR: a Noise Resilient Neuromorphic Event Representation

Gabriele Magrini, Federico Becattini, Luca Cultrera +3

Event cameras offer significant advantages over traditional frame-based sensors, including higher temporal resolution, lower latency and dynamic range. However, efficiently convert…

cs.CV2025

FRED: The Florence RGB-Event Drone Dataset

Gabriele Magrini, Niccolò Marini, Federico Becattini +4

Small, fast, and lightweight drones present significant challenges for traditional RGB cameras due to their limitations in capturing fast-moving objects, especially under challengi…

cs.CV2025

EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects

Gabriele Magrini, Federico Becattini, Giovanni Colombo +1

Monitoring aerial objects is crucial for security, wildlife conservation, and environmental studies. Traditional RGB-based approaches struggle with challenges such as scale variati…

cs.CV2024

Neuromorphic Drone Detection: an Event-RGB Multimodal Approach

Gabriele Magrini, Federico Becattini, Pietro Pala +2

In recent years, drone detection has quickly become a subject of extreme interest: the potential for fast-moving objects of contained dimensions to be used for malicious intents or…

cs.CV2023

Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving

Luca Cultrera, Federico Becattini, Lorenzo Seidenari +2

Conditional Imitation learning is a common and effective approach to train autonomous driving agents. However, two issues limit the full potential of this approach: (i) the inertia…

cs.CV2022

Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices

Benjamin Szczapa, Mohamed Daoudi, Stefano Berretti +3

We propose an automatic method to estimate self-reported pain based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different re…