Tobias Riedlinger

According to our database, Tobias Riedlinger authored at least 11 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

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., July, 2025

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs.

Hanno Gottschalk, Emil Partow, Tobias Riedlinger

CoRR, July, 2025

Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection.

Tobias Riedlinger, Kira Maag, Hanno Gottschalk

CoRR, June, 2025

Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration.

Emily C. Ehrhardt, Hanno Gottschalk, Tobias Riedlinger

CoRR, March, 2025

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

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Deep Active Learning with Noisy Oracle in Object Detection.

Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann

Proceedings of the 19th International Joint Conference on Computer Vision, 2024

Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection.

Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann

Proceedings of the 19th International Joint Conference on Computer Vision, 2024

Pixel-Wise Gradient Uncertainty for Convolutional Neural Networks Applied to Out-of-Distribution Segmentation.

Kira Maag, Tobias Riedlinger

Proceedings of the 19th International Joint Conference on Computer Vision, 2024

2023

Methods and applications of uncertainty quantification for object recognition.

Tobias Riedlinger

PhD thesis, 2023

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors.

Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

2022

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