22 papers
TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series
Gabor Szucs, Samuel Jacsev, Marcell Nemeth +2
Time series data play a pivotal role across numerous domains, including healthcare and manufacturing. In real-world environments, models must cope with distribution shifts over tim…
TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection
Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze +2
Detecting objects with visual sensors is crucial for numerous mobile robotics applications, from autonomous navigation to inspection. However, robots often need to operate under si…
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…