3 citations · 4 across the 4 of their papers we have counts for
5 papers
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…
Flexible Disaster Response of Tomorrow -- Final Presentation and Evaluation of the CENTAURO System
Tobias Klamt, Diego Rodriguez, Lorenzo Baccelliere +29
Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promisin…