7 papers
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…
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…
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…