74 citations · 148 across the 34 of their papers we have counts for
6 papers · 2 filters
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
Kira Maag, Matthias Rottmann, Serin Varghese +3
Instance segmentation with neural networks is an essential task in environment perception. In many works, it has been observed that neural networks can predict false positive insta…
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…
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
Kamil Kowol, Matthias Rottmann, Stefan Bracke +1
In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar…
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection
Marius Schubert, Karsten Kahl, Matthias Rottmann
In object detection with deep neural networks, the box-wise objectness score tends to be overconfident, sometimes even indicating high confidence in presence of inaccurate predicti…
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps
Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk +1
We present a novel region based active learning method for semantic image segmentation, called MetaBox+. For acquisition, we train a meta regression model to estimate the segment-w…
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
Philipp Oberdiek, Matthias Rottmann, Gernot A. Fink
When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts. These include, e.g., changes in weather conditions, time…