114 citations · 398 across the 14 of their papers we have counts for
14 papers
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra
Kanil Patel, William Beluch, Kilian Rambach +3
Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar.In addition to high accuracy, it is crucial for decision ma…
SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving
George Eskandar, Alexander Braun, Martin Meinke +2
Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision…
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
Bin Yang, Runsheng Guo, Ming Liang +2
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the…
Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
Karim Armanious, Aastha Tanwar, Sherif Abdulatif +3
Motion is one of the main sources for artifacts in magnetic resonance (MR) images. It can have significant consequences on the diagnostic quality of the resultant scans. Previously…
Unsupervised Medical Image Translation Using Cycle-MedGAN
Karim Armanious, Chenming Jiang, Sherif Abdulatif +3
Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-re…
Open-Set Recognition Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a…