38 citations · 46 across the 6 of their papers we have counts for
8 papers · 1 filter
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
Julian Burghoff, Robin Chan, Hanno Gottschalk +4
Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertai…
Detecting and Learning the Unknown in Semantic Segmentation
Robin Chan, Svenja Uhlemeyer, Matthias Rottmann +1
Semantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task and they are usually trained on a clos…
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer +6
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle pre…
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation
Robin Chan, Matthias Rottmann, Hanno Gottschalk
Deep neural networks (DNNs) for the semantic segmentation of images are usually trained to operate on a predefined closed set of object classes. This is in contrast to the "open wo…
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger +2
In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes. Neural networks are usually trained to reduce the over…
Detection of False Positive and False Negative Samples in Semantic Segmentation
Matthias Rottmann, Kira Maag, Robin Chan +3
In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology incl…