| Gradient-based quantification of epistemic uncertainty for deep object detectors T Riedlinger, M Rottmann, M Schubert, H Gottschalk Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 13 | 2023 |
| Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation K Maag, T Riedlinger arXiv preprint arXiv:2303.06920, 2023 | 3 | 2023 |
| Uncertainty quantification for object detection: output-and gradient-based approaches T Riedlinger, M Schubert, K Kahl, M Rottmann Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty …, 2022 | 3 | 2022 |
| Identifying Label Errors in Object Detection Datasets by Loss Inspection M Schubert, T Riedlinger, K Kahl, D Kröll, S Schoenen, S Šegvić, ... arXiv preprint arXiv:2303.06999, 2023 | 2 | 2023 |
| Deep Active Learning with Noisy Oracle in Object Detection M Schubert, T Riedlinger, K Kahl, M Rottmann arXiv preprint arXiv:2310.00372, 2023 | | 2023 |
| LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds T Riedlinger, M Schubert, S Penquitt, JM Kezmann, P Colling, K Kahl, ... arXiv preprint arXiv:2306.07835, 2023 | | 2023 |
| Methods and Applications of Uncertainty Quantification for Object Recognition T Riedlinger Universitätsbibliothek, 2023 | | 2023 |
| Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection T Riedlinger, M Schubert, K Kahl, H Gottschalk, M Rottmann arXiv preprint arXiv:2212.10836, 2022 | | 2022 |
| Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning J Burghoff, R Chan, H Gottschalk, A Muetze, T Riedlinger, M Rottmann, ... arXiv preprint arXiv:2205.14917, 2022 | | 2022 |
Matthias RottmannUniversity of WuppertalVerified email at math.uni-wuppertal.de
