11 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…
Multimodal Abstractive Summarization of Instructional Videos with Vision-Language Models
Maham Nazir, Muhammad Aqeel, Richong Zhang +1
Multimodal video summarization requires visual features that align semantically with language generation. Traditional approaches rely on CNN features trained for object classificat…
ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis
Muhammad Aqeel, Federico Leonardi, Francesco Setti
Industrial defect detection systems face critical limitations when confined to one-class anomaly detection paradigms, which assume uniform outlier distributions and struggle with d…
Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manua…