5 citations · 6 across the 9 of their papers we have counts for
12 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…
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…
Robust Anomaly Detection in Industrial Environments via Meta-Learning
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when tr…
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…
RoadFusion: Latent Diffusion Model for Pavement Defect Detection
Muhammad Aqeel, Kidus Dagnaw Bellete, Francesco Setti
Pavement defect detection faces critical challenges including limited annotated data, domain shift between training and deployment environments, and high variability in defect appe…
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…
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leve…
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect de…
Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection
Luigi Capogrosso, Federico Girella, Francesco Taioli +5
In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general,…