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cs.CV2026
LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing
Uzair Khan, Luigi Capogrosso, Muhammad Aqeel +3
In modern high-throughput industrial production lines, product configurations and visual characteristics frequently change, making it impractical to collect and annotate data for e…
cs.LG2026
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
Uzair Khan, Luigi Capogrosso, Francesco Biondani +4
Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across diffe…