activity
20212026
most citedLow-complexity acoustic scene classification in DCASE 2022 Challenge

22 citations · 25 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

GaLe: memory-efficient Global Approximate and Local Exact features

Alberto Ancilotto, Elisabetta Farella

Embedded devices typically lack the resources of GPU-equipped machines, and existing inference methods suffer from either high computational overhead (patch-based) or accuracy loss…

cs.NE2026

Elastic Spiking Transformers for Efficient Gesture Understanding

Alberto Ancilotto, Gianluca Amprimo, Stefano Di Carlo +1

Spiking Neural Networks (SNNs), particularly Spiking Transformers, offer energy-efficient processing of event-based sensor data for healthcare applications. Yet current architectur…

cs.CV2025★ 2 cited

EHWGesture -- A dataset for multimodal understanding of clinical gestures

Gianluca Amprimo, Alberto Ancilotto, Alessandro Savino +5

Hand gesture understanding is essential for several applications in human-computer interaction, including automatic clinical assessment of hand dexterity. While deep learning has a…

eess.AS2022★ 22 cited

Low-complexity acoustic scene classification in DCASE 2022 Challenge

Irene Martín-Morató, Francesco Paissan, Alberto Ancilotto +5

This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 2022 Challenge. The task was a continuation from the previous years, but the low-c…

cs.CV2021★ 1 cited

PhiNets: a scalable backbone for low-power AI at the edge

Francesco Paissan, Alberto Ancilotto, Elisabetta Farella

In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has becom…