3 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
Evidential Deep Learning for Class-Incremental Semantic Segmentation
Karl Holmquist, Lena Klasén, Michael Felsberg
Class-Incremental Learning is a challenging problem in machine learning that aims to extend previously trained neural networks with new classes. This is especially useful if the sy…
DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models
Karl Holmquist, Bastian Wandt
Traditionally, monocular 3D human pose estimation employs a machine learning model to predict the most likely 3D pose for a given input image. However, a single image can be highly…
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control
Zahra Gharaee, Karl Holmquist, Linbo He +1
In this paper, we present a state-of-the-art reinforcement learning method for autonomous driving. Our approach employs temporal difference learning in a Bayesian framework to lear…
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist +1
The focus in deep learning research has been mostly to push the limits of prediction accuracy. However, this was often achieved at the cost of increased complexity, raising concern…