25 citations · 62 across the 17 of their papers we have counts for
21 papers
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
Theodore Barfoot, Luis C. Garcia-Peraza-Herrera, Samet Akcay +2
Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulation…
FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
Brian K. S. Isaac-Medina, Mauricio Che, Yona F. A. Gaus +2
Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distributio…
Anomalib: A Deep Learning Library for Anomaly Detection
Samet Akcay, Dick Ameln, Ashwin Vaidya +3
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides…
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This paper presents a novel strategy that extend…
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper,…
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Identifying potential threats concealed within the baggage is of prime concern for the security staff. Many researchers have developed frameworks that can detect baggage threats fr…