3 citations · 13 across the 28 of their papers we have counts for
19 papers · 1 filter
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…
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…
Explainable Visual Anomaly Detection via Concept Bottleneck Models
Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti +3
In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanations by highlighting anomalous r…
MoViAD: A Modular Library for Visual Anomaly Detection
Manuel Barusco, Francesco Borsatti, Arianna Stropeni +2
VAD is a critical field in machine learning focused on identifying deviations from normal patterns in images, often challenged by the scarcity of anomalous data and the need for un…
Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing
Adrian Poniatowski, Natalie Gentner, Manuel Barusco +3
In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant market share in vario…
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study
Manuel Barusco, Francesco Borsatti, Youssef Ben Khalifa +2
Semiconductor manufacturing is a complex, multistage process. Automated visual inspection of Scanning Electron Microscope (SEM) images is indispensable for minimizing equipment dow…
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
Arianna Stropeni, Francesco Borsatti, Manuel Barusco +3
Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT)…
Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection
Riccardo De Monte, Davide Dalle Pezze, Gian Antonio Susto
Real-time object detectors like YOLO achieve exceptional performance when trained on large datasets for multiple epochs. However, in real-world scenarios where data arrives increme…
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection
Manuel Barusco, Lorenzo D'Antoni, Davide Dalle Pezze +2
Visual Anomaly Detection (VAD) is a critical task in computer vision with numerous real-world applications. However, deploying these models on edge devices presents significant cha…
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…
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris +1
Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in re…
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
Nikola Bugarin, Jovana Bugaric, Manuel Barusco +2
Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes ov…
Continual Learning Approaches for Anomaly Detection
Davide Dalle Pezze, Eugenia Anello, Chiara Masiero +1
Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task…