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Identifying Label Errors in Object Detection Datasets by Loss Inspection


Mar 13, 2023
Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Siniša Šegvić, Matthias Rottmann

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AttEntropy: Segmenting Unknown Objects in Complex Scenes using the Spatial Attention Entropy of Semantic Segmentation Transformers


Dec 29, 2022
Krzysztof Lis, Matthias Rottmann, Sina Honari, Pascal Fua, Mathieu Salzmann

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


Dec 21, 2022
Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann

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* 17 pages, 12 figures, 9 tables 

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MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions


Nov 10, 2022
Antonia van Betteray, Matthias Rottmann, Karsten Kahl

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Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss


Aug 18, 2022
Annika Mütze, Matthias Rottmann, Hanno Gottschalk

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* 11pages, 11figures 

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Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification


Jul 13, 2022
Matthias Rottmann, Marco Reese

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False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation


Jul 07, 2022
Kira Maag, Matthias Rottmann

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What should AI see? Using the Public's Opinion to Determine the Perception of an AI


Jun 09, 2022
Robin Chan, Radin Dardashti, Meike Osinski, Matthias Rottmann, Dominik Brüggemann, Cilia Rücker, Peter Schlicht, Fabian Hüger, Nikol Rummel, Hanno Gottschalk

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* 26 pages, 12 figures 

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Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning


May 30, 2022
Julian Burghoff, Robin Chan, Hanno Gottschalk, Annika Muetze, Tobias Riedlinger, Matthias Rottmann, Marius Schubert

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Detecting and Learning the Unknown in Semantic Segmentation


Feb 17, 2022
Robin Chan, Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk

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* 37 pages, 7 figures, chapter in Deep Neural Networks and Data for Automated Driving 

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