Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty
Source's Perspective Page 1 A Survey on Uncertainty Quantification Methods for Deep Neural …

Uncertainty quantification is crucial for the deployment of image restoration models in safety-
critical domains, like autonomous driving and biological imaging. To date, methods for …

In autonomous driving, monocular 3D object detection is an important but challenging task.
Towards accurate monocular 3D object detection, some recent methods recover the …

We propose learning a depth covariance function with applications to geometric vision tasks.
Given RGB images as input, the covariance function can be flexibly used to define priors …

In this paper, we propose a new framework for point cloud instance segmentation. Our
framework has two steps: an embedding step and a clustering step. In the embedding step …

Deep learning algorithms have the potential to automate the examination of medical images
obtained in clinical practice. Using digitized medical images, convolution neural networks …

Railway networks systems are by design open and accessible to people, but this presents
challenges in the prevention of events such as terrorism, trespass, and suicide fatalities …

Learning computational models for visual attention (saliency estimation) is an effort to inch
machines/robots closer to human visual cognitive abilities. Data-driven efforts have …