6 citations · 7 across the 9 of their papers we have counts for
13 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…
Diffusion-Based Data Augmentation for Medical Image Segmentation
Maham Nazir, Muhammad Aqeel, Francesco Setti
Medical image segmentation models struggle with rare abnormalities due to scarce annotated pathological data. We propose DiffAug a novel framework that combines textguided diffusio…
A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
Muhammad Aqeel, Danijel Skocaj, Marco Cristani +1
Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settin…