114 citations · 579 across the 79 of their papers we have counts for
7 papers · 1 filter
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…
TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training
Li Li, Tanqiu Qiao, Hubert P. H. Shum +1
3D point clouds are essential for perceiving outdoor scenes, especially within the realm of autonomous driving. Recent advances in 3D LiDAR Object Detection focus primarily on the…
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…
RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
Li Li, Hubert P. H. Shum, Toby P. Breckon
3D point clouds play a pivotal role in outdoor scene perception, especially in the context of autonomous driving. Recent advancements in 3D LiDAR segmentation often focus intensely…
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications
Li Li, Khalid N. Ismail, Hubert P. H. Shum +1
We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth esti…
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…
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed +1
Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement…