5 citations · 5 across the 3 of their papers we have counts for
5 papers
Is SAM3 ready for pathology segmentation?
Qiuyu Kong, Shakiba Sharifi, Yiming Wang +2
Is Segment Anything Model 3 (SAM3) capable in segmenting Any Pathology Images? Digital pathology segmentation spans tissue-level and nuclei-level scales, where traditional methods…
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…
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…