1 citations · 2 across the 17 of their papers we have counts for
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Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing
Davide Frizzo, Francesco Borsatti, Gian Antonio Susto
Semiconductor manufacturing relies on tightly interconnected components, so early identification of the assets most likely to fail is essential to prevent a single breakdown from d…
AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving
Fabrizio Genilotti, Arianna Stropeni, Gionata Grotto +4
The reliability of a machine vision system for autonomous driving depends heavily on its training data distribution. When a vehicle encounters significantly different conditions, s…
Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions
Manuel Barusco, Francesco Borsatti, David Petrovic +2
Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare. While VAD has been extensively studied, two key challenges r…
Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices
Arianna Stropeni, Fabrizio Genilotti, Francesco Borsatti +3
Visual Anomaly Detection (VAD) is essential for industrial quality control, enabling automatic defect detection in manufacturing. In real production lines, VAD systems must satisfy…
AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection
Manuel Barusco, Davide Dalle Pezze, Francesco Borsatti +1
Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to address the multi-class and cont…
Deep Learning for Virtual Reality User Identification: A Benchmark
Davide Frizzo, Fabrizio Genilotti, David Petrovic +6
Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motion tracking data from VR headse…
VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery
Fabrizio Genilotti, Arianna Stropeni, Francesco Borsatti +3
Space missions generate massive volumes of high-resolution orbital and surface imagery that far exceed the capacity for manual inspection. Detecting rare phenomena is scientificall…
MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection
Jinwei Hu, Francesco Borsatti, Arianna Stropeni +3
Industrial visual anomaly detection (VAD) methods are typically trained on normal samples only, yet performance improves substantially when even limited anomalous data is available…