4 papers
Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection
Brian K. S. Isaac-Medina, Toby P. Breckon
Deep neural networks have demonstrated great generalization capabilities for tasks whose training and test sets are drawn from the same distribution. Nevertheless, out-of-distribut…
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…
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis
Brian K. S. Isaac-Medina, Yona Falinie A. Gaus, Neelanjan Bhowmik +1
Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out o…
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina +1
The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot s…