3 citations · 3 across the 6 of their papers we have counts for
7 papers
Deep Active Learning with Noisy Oracle in Object Detection
Marius Schubert, Tobias Riedlinger, Karsten Kahl +1
Obtaining annotations for complex computer vision tasks such as object detection is an expensive and time-intense endeavor involving a large number of human workers or expert opini…
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Tobias Riedlinger, Marius Schubert, Sarina Penquitt +7
Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent…
Identifying Label Errors in Object Detection Datasets by Loss Inspection
Marius Schubert, Tobias Riedlinger, Karsten Kahl +4
Labeling datasets for supervised object detection is a dull and time-consuming task. Errors can be easily introduced during annotation and overlooked during review, yielding inaccu…
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…
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors
Tobias Riedlinger, Matthias Rottmann, Marius Schubert +1
The vast majority of uncertainty quantification methods for deep object detectors such as variational inference are based on the network output. Here, we study gradient-based epist…
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…