Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

This review comprehensively investigates the current state and emerging trends of
autonomous vehicle terrain detection and segmentation. By systematically reviewing …

In multi-label classification, each example in a dataset may be annotated as belonging to
one or more classes (or none of the classes). Example applications include image (or …

Autolabeling approaches are attractive wrt time and cost as they allow fast annotation
without human intervention. However, can we really trust the label quality of autolabeling …

Domain adaptation is of huge interest as labeling is an expensive and error-prone task,
especially when labels are needed on pixel-level like in semantic segmentation. Therefore …

Labeling datasets for supervised object detection is a dull and time-consuming task. Errors
can be easily introduced during annotation and overlooked during review, yielding …

Training and validating models for semantic segmentation require datasets with pixel-wise
annotations, which are notoriously labor-intensive. Although useful priors such as …

Domain adaptation is of huge interest as labeling is an expensive and error-prone task,
especially on pixellevel like for semantic segmentation. Therefore, one would like to train …

Quantifying predictive uncertainty of deep semantic segmentation networks is essential in
safety-critical tasks. In applications like autonomous driving, where video data is available …

When employing deep neural networks (DNNs) for semantic segmentation in safety-critical
applications like automotive perception or medical imaging, it is important to estimate their …