5 papers
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…
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…
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Muhammad Aqeel, Maham Nazir, Uzair Khan +2
Zero-shot anomaly detection aims to identify defects in unseen categories without target-specific training. Existing methods usually apply the same feature transformation to all sa…
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices
Uzair Khan, Franco Fummi, Luigi Capogrosso
In the era of intelligent manufacturing, anomaly detection has become essential for maintaining quality control on modern production lines. However, while many existing models show…
Material synthesis through simulations guided by machine learning: a position paper
Usman Syed, Federico Cunico, Uzair Khan +6
In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual fr…