3 citations · 5 across the 4 of their papers we have counts for
6 papers
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Qin Wang +2
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Qin Wang, Dengxin Dai, Lukas Hoyer +2
Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervisi…
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Yuhua Chen +3
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
Grid Saliency for Context Explanations of Semantic Segmentation
Lukas Hoyer, Mauricio Munoz, Prateek Katiyar +2
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limi…
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
Lukas Hoyer, Patrick Kesper, Anna Khoreva +1
An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decisi…
A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation
Lukas Hoyer, Christoph Steup, Sanaz Mostaghim
External localization is an essential part for the indoor operation of small or cost-efficient robots, as they are used, for example, in swarm robotics. We introduce a two-stage lo…