Tobias Riedlinger

20 papers A 2Journal 13Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs. CoRR Jonathan Klees, Tobias Riedlinger, Peter Stehr, Bennet Böddecker, Daniel Kondermann, Matthias Rottmann
2025	J	`jnl`	Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs. CoRR Hanno Gottschalk, Emil Partow, Tobias Riedlinger
2025	J	`jnl`	LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds. Int. J. Comput. Vis. Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann
2025	J	`jnl`	Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets. CoRR Sarina Penquitt, Tobias Riedlinger, Timo Heller, Markus Reischl, Matthias Rottmann
2025	J	`jnl`	Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration. CoRR Emily C. Ehrhardt, Hanno Gottschalk, Tobias Riedlinger
2025	J	`jnl`	Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection. CoRR Tobias Riedlinger, Kira Maag, Hanno Gottschalk
2024	—	`conf`	Deep Active Learning with Noisy Oracle in Object Detection. VISIGRAPP (2): VISAPP Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann
2024	A	`conf`	Identifying Label Errors in Object Detection Datasets by Loss Inspection. WACV Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Sinisa Segvic, Matthias Rottmann
2024	—	`conf`	Pixel-Wise Gradient Uncertainty for Convolutional Neural Networks Applied to Out-of-Distribution Segmentation. VISIGRAPP (2): VISAPP Kira Maag, Tobias Riedlinger
2024	—	`conf`	Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. VISIGRAPP (2): VISAPP Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann
2023	J	`jnl`	Deep Active Learning with Noisy Oracle in Object Detection. CoRR Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann
2023	A	`conf`	Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. WACV Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk
2023	J	`jnl`	Identifying Label Errors in Object Detection Datasets by Loss Inspection. CoRR Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Sinisa Segvic, Matthias Rottmann
2023	—	`conf`	LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds. DAGM Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann
2023	J	`jnl`	LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds. CoRR Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann
2023	—	`—`	Methods and applications of uncertainty quantification for object recognition. Tobias Riedlinger
2023	J	`jnl`	Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation. CoRR Kira Maag, Tobias Riedlinger
2022	J	`jnl`	Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. CoRR Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann
2022	J	`jnl`	Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning. CoRR Julian Burghoff, Robin Chan, Hanno Gottschalk, Annika Mütze, Tobias Riedlinger, Matthias Rottmann, Marius Schubert
2021	J	`jnl`	Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. CoRR Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk