74 citations · 360 across the 51 of their papers we have counts for
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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…
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
Kira Maag, Matthias Rottmann, Hanno Gottschalk
In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest. The assessment of neural networks by means of uncertainti…
The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation
Robin Chan, Matthias Rottmann, Radin Dardashti +3
Neural networks for semantic segmentation can be seen as statistical models that provide for each pixel of one image a probability distribution on predefined classes. The predicted…
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images
Matthias Rottmann, Marius Schubert
In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for e…
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger +2
As part of autonomous car driving systems, semantic segmentation is an essential component to obtain a full understanding of the car's environment. One difficulty, that occurs whil…