51 citations · 338 across the 88 of their papers we have counts for
11 papers · 1 filter
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…
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning
Hayk Asatryan, Hanno Gottschalk, Marieke Lippert +1
In recent years, generative adversarial networks (GANs) have demonstrated impressive experimental results while there are only a few works that foster statistical learning theory f…
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…
Integrability and Approximability of Solutions to the Stationary Diffusion Equation with Lévy Coefficient
Oliver G. Ernst, Hanno Gottschalk, Thomas Kalmes +2
We investigate the stationary diffusion equation with a coefficient given by a (transformed) Lévy random field. Lévy random fields are constructed by smoothing Lévy noise fields wi…