Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images. - Matthias Rottmann; Marius Schubert (CVPR Workshops, 2019)

MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection. - Marius Schubert; Karsten Kahl; Matthias Rottmann (IJCNN, 2021)

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)

Uncertainty Quantification Methods and their Applications in Object Detection. - Marius Schubert (2023)

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)

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)