Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning. - Julian Burghoff; Robin Chan; Hanno Gottschalk; Annika Mütze; Tobias Riedlinger; Matthias Rottmann; Marius Schubert (CoRR, 2022)

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. - Tobias Riedlinger; Matthias Rottmann; Marius Schubert; Hanno Gottschalk (WACV, 2023)

Methods and applications of uncertainty quantification for object recognition. - Tobias Riedlinger (2023)

Pixel-Wise Gradient Uncertainty for Convolutional Neural Networks Applied to Out-of-Distribution Segmentation. - Kira Maag; Tobias Riedlinger (VISIGRAPP (2): VISAPP, 2024)

Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. - Tobias Riedlinger; Marius Schubert; Karsten Kahl; Hanno Gottschalk; Matthias Rottmann (VISIGRAPP (2): VISAPP, 2024)

Deep Active Learning with Noisy Oracle in Object Detection. - Marius Schubert; Tobias Riedlinger; Karsten Kahl; Matthias Rottmann (VISIGRAPP (2): VISAPP, 2024)

Identifying Label Errors in Object Detection Datasets by Loss Inspection. - Marius Schubert; Tobias Riedlinger; Karsten Kahl; Daniel Kröll; Sebastian Schoenen; Sinisa Segvic; Matthias Rottmann (WACV, 2024)

Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration. - Emily C. Ehrhardt; Hanno Gottschalk; Tobias Riedlinger (CoRR, 2025)

Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection. - Tobias Riedlinger; Kira Maag; Hanno Gottschalk (CoRR, 2025)

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs. - Hanno Gottschalk; Emil Partow; Tobias Riedlinger (CoRR, 2025)

LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds. - Tobias Riedlinger; Marius Schubert; Sarina Penquitt; Jan-Marcel Kezmann; Pascal Colling; Karsten Kahl; Lutz Roese-Koerner; Michael Arnold; Urs Zimmermann; Matthias Rottmann (Int. J. Comput. Vis., 2025)