6 citations · 18 across the 6 of their papers we have counts for
6 papers
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
Arvi Jonnarth, Ola Johansson, Jie Zhao +1
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and…
Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
Arvi Jonnarth, Jie Zhao, Michael Felsberg
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and…
Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
Ziliang Xiong, Arvi Jonnarth, Abdelrahman Eldesokey +3
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it…
High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation
Arvi Jonnarth, Yushan Zhang, Michael Felsberg
Image-level weakly-supervised semantic segmentation (WSSS) reduces the usually vast data annotation cost by surrogate segmentation masks during training. The typical approach invol…
Balanced Product of Calibrated Experts for Long-Tailed Recognition
Emanuel Sanchez Aimar, Arvi Jonnarth, Michael Felsberg +1
Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited genera…
Importance Sampling CAMs for Weakly-Supervised Segmentation
Arvi Jonnarth, Michael Felsberg
Classification networks can be used to localize and segment objects in images by means of class activation maps (CAMs). However, without pixel-level annotations, classification net…