8 citations · 11 across the 12 of their papers we have counts for
13 papers
Regularity of Solutions to Beckmann's Parametric Optimal Transport
Hanno Gottschalk, Tobias J. Riedlinger
Beckmann's problem in optimal transport minimizes the total squared flux in a continuous transport problem from a source to a target distribution. In this article, the regularity t…
Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs
Jonathan Klees, Tobias Riedlinger, Peter Stehr +3
Safe artificial intelligence for perception tasks remains a major challenge, partly due to the lack of data with high-quality labels. Annotations themselves are subject to aleatori…
Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets
Sarina Penquitt, Tobias Riedlinger, Timo Heller +2
Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and indust…
Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs
Hanno Gottschalk, Emil Partow, Tobias J. Riedlinger
We prove consistency of a recently proposed scheme that evaluates expected values by composing a learned transport map with Clenshaw--Curtis sparse-grid quadrature on a tractable p…
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Tobias J. Riedlinger, Kira Maag, Hanno Gottschalk
Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation. Object detectors and segmentation models assig…
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration
Emily C. Ehrhardt, Hanno Gottschalk, Tobias J. Riedlinger
NeuralODE is one example for generative machine learning based on the push forward of a simple source measure with a bijective mapping, which in the case of NeuralODE is given by t…
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…
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation
Kira Maag, Tobias Riedlinger
In recent years, deep neural networks have defined the state-of-the-art in semantic segmentation where their predictions are constrained to a predefined set of semantic classes. Th…
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection
Tobias Riedlinger, Marius Schubert, Karsten Kahl +2
Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and…
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…