AI RESEARCH
Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions
arXiv CS.CV
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ArXi:2604.06435v1 Announce Type: new 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.