1 citations · 2 across the 5 of their papers we have counts for
6 papers
Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions
Tobias Kalb, Jürgen Beyerer
Deep neural networks for scene perception in automated vehicles achieve excellent results for the domains they were trained on. However, in real-world conditions, the domain of ope…
Effects of Architectures on Continual Semantic Segmentation
Tobias Kalb, Niket Ahuja, Jingxing Zhou +1
Research in the field of Continual Semantic Segmentation is mainly investigating novel learning algorithms to overcome catastrophic forgetting of neural networks. Most recent publi…
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection
Tobias Kalb, Björn Mauthe, Jürgen Beyerer
Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new class…
Continual Learning for Class- and Domain-Incremental Semantic Segmentation
Tobias Kalb, Masoud Roschani, Miriam Ruf +1
The field of continual deep learning is an emerging field and a lot of progress has been made. However, concurrently most of the approaches are only tested on the task of image cla…
Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation
Tobias Kalb, Jürgen Beyerer
Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new…
Human Pose Estimation for Real-World Crowded Scenarios
Thomas Golda, Tobias Kalb, Arne Schumann +1
Human pose estimation has recently made significant progress with the adoption of deep convolutional neural networks. Its many applications have attracted tremendous interest in re…