[2604.06435] Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.06435 (cs) [Submitted on 7 Apr 2026] Title:Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions Authors:Manuel Barusco, Francesco Borsatti, David Petrovic, Davide Dalle Pezze, Gian Antonio Susto View a PDF of the paper titled Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions, by Manuel Barusco and 4 other authors View PDF HTML (experimental) Abstract: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 remain largely unaddressed in conjunction: edge deployment, where computational resources are severely constrained, and continual learning, where models must adapt to evolving data distributions without forgetting previously acquired knowledge. Our benchmark provides guidance for the selection of the optimal backbone and VAD method under joint efficiency and adaptability constraints, characterizing the trade-offs between memory footprint, inference cost, and detection performance. Studying these challenges in isolation is insufficient, as methods designed for one setting make assumptions that break down when the other constraint is simultaneously imposed. In this work, we propose the first comprehensive benchmark for VAD on the edge in the continual learning scenario, evaluating seven VAD models across three lightweight ba...