5 citations · 8 across the 9 of their papers we have counts for
10 papers
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…
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…
Explicit modelling of subject dependency in BCI decoding
Michele Romani, Francesco Paissan, Andrea Fossà +1
Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to…
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…
A probabilistic framework for dynamic quantization
Gabriele Santini, Francesco Paissan, Elisabetta Farella
We propose a probabilistic framework for dynamic quantization of neural networks that allows for a computationally efficient input-adaptive rescaling of the quantization parameters…
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms
Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3
Recent advances in Visual Anomaly Detection (VAD) have introduced sophisticated algorithms leveraging embeddings generated by pre-trained feature extractors. Inspired by these deve…
PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge
Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3
Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A…
Latent Distillation for Continual Object Detection at the Edge
Francesco Pasti, Marina Ceccon, Davide Dalle Pezze +4
While numerous methods achieving remarkable performance exist in the Object Detection literature, addressing data distribution shifts remains challenging. Continual Learning (CL) o…
Replay Consolidation with Label Propagation for Continual Object Detection
Riccardo De Monte, Davide Dalle Pezze, Marina Ceccon +5
Continual Learning (CL) aims to learn new data while remembering previously acquired knowledge. In contrast to CL for image classification, CL for Object Detection faces additional…
tinyCLAP: Distilling Constrastive Language-Audio Pretrained Models
Francesco Paissan, Elisabetta Farella
Contrastive Language-Audio Pretraining (CLAP) became of crucial importance in the field of audio and speech processing. Its employment ranges from sound event detection to text-to-…