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ORCIDMatthias Rottmann
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2020 – today
- 2024
[c33]Edgar Heinert, Matthias Rottmann, Kira Maag, Karsten Kahl:
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion. ECAI 2024: 609-617- [c32]Jialong Wu, Mirko Meuter, Markus Schoeler, Matthias Rottmann:
SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data. ECCV (86) 2024: 52-69 - [c31]Julian Burghoff, Matthias Rottmann, Jill von Conta, Sebastian Schoenen, Andreas Witte, Hanno Gottschalk:
ResBuilder: Automated Learning of Depth with Residual Structures. ICANN (1) 2024: 308-323 - [c30]Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann:
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. VISIGRAPP (2): VISAPP 2024: 366-374 - [c29]Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann:
Deep Active Learning with Noisy Oracle in Object Detection. VISIGRAPP (2): VISAPP 2024: 375-384 - [c28]Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Sinisa Segvic, Matthias Rottmann:
Identifying Label Errors in Object Detection Datasets by Loss Inspection. WACV 2024: 4570-4579 - [i47]Edgar Heinert, Matthias Rottmann, Kira Maag, Karsten Kahl:
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion. CoRR abs/2402.09530 (2024) - [i46]Jialong Wu, Mirko Meuter, Markus Schoeler, Matthias Rottmann:
SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data. CoRR abs/2406.10600 (2024) - [i45]Alexey Nekrasov, Rui Zhou, Miriam Ackermann, Alexander Hermans, Bastian Leibe, Matthias Rottmann:
OoDIS: Anomaly Instance Segmentation Benchmark. CoRR abs/2406.11835 (2024) - [i44]Sadia Ilyas, Ido Freeman, Matthias Rottmann:
On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenes. CoRR abs/2408.11221 (2024) - [i43]Edgar Heinert, Stephan Tilgner, Timo Palm, Matthias Rottmann:
Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks. CoRR abs/2409.11373 (2024) - [i42]Mert Keser, Gesina Schwalbe, Niki Amini-Naieni, Matthias Rottmann, Alois Knoll:
Unveiling Ontological Commitment in Multi-Modal Foundation Models. CoRR abs/2409.17109 (2024) - 2023
- [j8]Robin Chan
, Radin Dardashti, Meike Osinski, Matthias Rottmann, Dominik Brüggemann, Cilia Rücker, Peter Schlicht, Fabian Hüger, Nikol Rummel, Hanno Gottschalk:
What should AI see? Using the public's opinion to determine the perception of an AI. AI Ethics 3(4): 1381-1405 (2023) - [j7]Jesus Espinoza-Valverde, Andreas Frommer, Gustavo Ramirez-Hidalgo, Matthias Rottmann:
Coarsest-level improvements in multigrid for lattice QCD on large-scale computers. Comput. Phys. Commun. 292: 108869 (2023) - [j6]Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk:
Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds. Int. J. Artif. Intell. Tools 32(5): 2360006:1-2360006:25 (2023) - [j5]Matthias Rottmann, Kira Maag, Mathis Peyron, Hanno Gottschalk, Natasa Krejic:
Detection of Iterative Adversarial Attacks via Counter Attack. J. Optim. Theory Appl. 198(3): 892-929 (2023) - [c27]Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann:
LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds. DAGM 2023: 85-99 - [c26]Antonia van Betteray, Matthias Rottmann, Karsten Kahl:
MGiaD: Multigrid in all dimensions. Efficiency and robustness by weight sharing and coarsening in resolution and channel dimensions. ICCV (Workshops) 2023: 1284-1293 - [c25]Annika Mütze, Matthias Rottmann, Hanno Gottschalk:
Semi-Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness. VISIGRAPP (5: VISAPP) 2023: 80-90 - [c24]Kira Maag, Matthias Rottmann:
False Negative Reduction in Semantic Segmentation Under Domain Shift Using Depth Estimation. VISIGRAPP (5: VISAPP) 2023: 397-408 - [c23]Matthias Rottmann, Marco Reese:
Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification. WACV 2023: 3213-3222 - [c22]Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk:
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. WACV 2023: 3910-3920 - [i41]Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Sinisa Segvic, Matthias Rottmann:
Identifying Label Errors in Object Detection Datasets by Loss Inspection. CoRR abs/2303.06999 (2023) - [i40]Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann:
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds. CoRR abs/2306.07835 (2023) - [i39]Julian Burghoff, Matthias Rottmann, Jill von Conta, Sebastian Schoenen, Andreas Witte, Hanno Gottschalk:
ResBuilder: Automated Learning of Depth with Residual Structures. CoRR abs/2308.08504 (2023) - [i38]Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann:
Deep Active Learning with Noisy Oracle in Object Detection. CoRR abs/2310.00372 (2023) - [i37]Lorenc Kapllani, Long Teng, Matthias Rottmann:
Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations. CoRR abs/2310.03393 (2023) - 2022
- [c21]Pascal Colling, Dennis Müller, Matthias Rottmann:
HD Lane Map Generation Based on Trail Map Aggregation. IV 2022: 600-606 - [c20]Philipp Oberdiek, Gernot A. Fink, Matthias Rottmann:
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs. NeurIPS 2022 - [c19]Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Towards unsupervised open world semantic segmentation. UAI 2022: 1981-1991 - [i36]Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Towards Unsupervised Open World Semantic Segmentation. CoRR abs/2201.01073 (2022) - [i35]Philipp Oberdiek, Gernot A. Fink, Matthias Rottmann:
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs. CoRR abs/2201.13279 (2022) - [i34]Robin Chan, Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Detecting and Learning the Unknown in Semantic Segmentation. CoRR abs/2202.08700 (2022) - [i33]Jesus Espinoza-Valverde, Andreas Frommer, Gustavo Ramirez-Hidalgo, Matthias Rottmann:
Coarsest-level improvements in multigrid for lattice QCD on large-scale computers. CoRR abs/2205.09104 (2022) - [i32]Julian Burghoff, Robin Chan, Hanno Gottschalk, Annika Mütze, Tobias Riedlinger, Matthias Rottmann, Marius Schubert:
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning. CoRR abs/2205.14917 (2022) - [i31]Robin Chan, Radin Dardashti, Meike Osinski, Matthias Rottmann, Dominik Brüggemann, Cilia Rücker, Peter Schlicht, Fabian Hüger, Nikol Rummel, Hanno Gottschalk:
What should AI see? Using the Public's Opinion to Determine the Perception of an AI. CoRR abs/2206.04776 (2022) - [i30]Kira Maag, Matthias Rottmann:
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation. CoRR abs/2207.03513 (2022) - [i29]Matthias Rottmann, Marco Reese:
Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification. CoRR abs/2207.06104 (2022) - [i28]Annika Mütze, Matthias Rottmann, Hanno Gottschalk:
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss. CoRR abs/2208.08815 (2022) - [i27]Antonia van Betteray, Matthias Rottmann, Karsten Kahl:
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions. CoRR abs/2211.05525 (2022) - [i26]Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann:
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. CoRR abs/2212.10836 (2022) - [i25]Krzysztof Lis, Matthias Rottmann, Sina Honari, Pascal Fua, Mathieu Salzmann:
AttEntropy: Segmenting Unknown Objects in Complex Scenes using the Spatial Attention Entropy of Semantic Segmentation Transformers. CoRR abs/2212.14397 (2022) - 2021
- [j4]Andreas Frommer, Karsten Kahl, Francesco Knechtli, Matthias Rottmann, Artur Strebel, Ian Zwaan:
A multigrid accelerated eigensolver for the Hermitian Wilson-Dirac operator in lattice QCD. Comput. Phys. Commun. 258: 107615 (2021) - [c18]Kamil Kowol, Matthias Rottmann, Stefan Bracke, Hanno Gottschalk:
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors. ICAART (2) 2021: 177-186 - [c17]Robin Chan, Matthias Rottmann, Hanno Gottschalk:
Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. ICCV 2021: 5108-5117 - [c16]Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, Matthias Rottmann:
MetaBox+: A New Region based Active Learning Method for Semantic Segmentation using Priority Maps. ICPRAM 2021: 51-62 - [c15]Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk:
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation. ICTAI 2021: 18-25 - [c14]Kira Maag, Matthias Rottmann, Serin Varghese, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. IJCNN 2021: 1-8 - [c13]Marius Schubert, Karsten Kahl, Matthias Rottmann:
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection. IJCNN 2021: 1-10 - [c12]Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka, Maram Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, Fabian Hüger, Matthias Rottmann, Sebastian Houben, Tim Wirtz:
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis. IV Workshops 2021: 182-189 - [c11]Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, Matthias Rottmann:
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. NeurIPS Datasets and Benchmarks 2021 - [i24]Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle:
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. CoRR abs/2104.14235 (2021) - [i23]Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Mathieu Salzmann, Pascal Fua, Matthias Rottmann:
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. CoRR abs/2104.14812 (2021) - [i22]Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka, Maram Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, Fabian Hüger, Matthias Rottmann, Sebastian Houben, Tim Wirtz:
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis. CoRR abs/2106.05549 (2021) - [i21]Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk:
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. CoRR abs/2107.04517 (2021) - [i20]Claudia Drygala, Matthias Rottmann, Hanno Gottschalk, Klaus Friedrichs, Thomas Kurbiel:
Background-Foreground Segmentation for Interior Sensing in Automotive Industry. CoRR abs/2109.09410 (2021) - [i19]Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk:
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation. CoRR abs/2110.15681 (2021) - [i18]Hanno Gottschalk, Matthias Rottmann, Maida Saltagic:
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance? CoRR abs/2112.04758 (2021) - 2020
- [c10]Philipp Oberdiek, Matthias Rottmann, Gernot A. Fink:
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation. CVPR Workshops 2020: 1331-1340 - [c9]Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Detection of False Positive and False Negative Samples in Semantic Segmentation. DATE 2020: 1351-1356 - [c8]Kira Maag, Matthias Rottmann, Hanno Gottschalk:
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. ICTAI 2020: 502-509 - [c7]Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. IJCNN 2020: 1-8 - [c6]Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. IJCNN 2020: 1-9 - [i17]Philipp Oberdiek, Matthias Rottmann, Gernot A. Fink:
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation. CoRR abs/2005.06831 (2020) - [i16]Matthias Rottmann, Mathis Peyron, Natasa Krejic, Hanno Gottschalk:
Detection of Iterative Adversarial Attacks via Counter Attack. CoRR abs/2009.11397 (2020) - [i15]Marius Schubert, Karsten Kahl, Matthias Rottmann:
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection. CoRR abs/2010.01695 (2020) - [i14]Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, Matthias Rottmann:
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps. CoRR abs/2010.01884 (2020) - [i13]Kamil Kowol, Matthias Rottmann, Stefan Bracke, Hanno Gottschalk:
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors. CoRR abs/2010.03320 (2020) - [i12]Hayk Asatryan, Hanno Gottschalk, Marieke Lippert, Matthias Rottmann:
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning. CoRR abs/2011.12087 (2020) - [i11]Robin Chan, Matthias Rottmann, Hanno Gottschalk:
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation. CoRR abs/2012.06575 (2020) - [i10]Kira Maag, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. CoRR abs/2012.07504 (2020)
2010 – 2019
- 2019
- [c5]Matthias Rottmann, Marius Schubert:
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images. CVPR Workshops 2019: 1361-1369 - [c4]Robin Chan, Matthias Rottmann, Radin Dardashti, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation. CVPR Workshops 2019: 1395-1403 - [c3]Andreas Behrend, Anton Dignös, Johann Gamper, Philip Schmiegelt, Hannes Voigt, Matthias Rottmann, Karsten Kahl:
Period Index: A Learned 2D Hash Index for Range and Duration Queries. SSTD 2019: 100-109 - [i9]Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation. CoRR abs/1901.08394 (2019) - [i8]Matthias Rottmann, Marius Schubert:
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images. CoRR abs/1904.04516 (2019) - [i7]Robin Chan, Matthias Rottmann, Radin Dardashti, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation. CoRR abs/1907.01342 (2019) - [i6]Kira Maag, Matthias Rottmann, Hanno Gottschalk:
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. CoRR abs/1911.05075 (2019) - [i5]Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Detection of False Positive and False Negative Samples in Semantic Segmentation. CoRR abs/1912.03673 (2019) - [i4]Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation. CoRR abs/1912.07420 (2019) - 2018
- [j3]Karsten Kahl, Matthias Rottmann:
Least Angle Regression Coarsening in Bootstrap Algebraic Multigrid. SIAM J. Sci. Comput. 40(6): A3928-A3954 (2018) - [c2]Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk:
Classification Uncertainty of Deep Neural Networks Based on Gradient Information. ANNPR 2018: 113-125 - [c1]Matthias Rottmann, Karsten Kahl, Hanno Gottschalk:
Deep Bayesian Active Semi-Supervised Learning. ICMLA 2018: 158-164 - [i3]Matthias Rottmann, Karsten Kahl, Hanno Gottschalk:
Deep Bayesian Active Semi-Supervised Learning. CoRR abs/1803.01216 (2018) - [i2]Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk:
Classification Uncertainty of Deep Neural Networks Based on Gradient Information. CoRR abs/1805.08440 (2018) - [i1]Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. CoRR abs/1811.00648 (2018) - 2016
- [j2]James J. Brannick, Andreas Frommer, Karsten Kahl, Björn Leder, Matthias Rottmann, Artur Strebel:
Multigrid preconditioning for the overlap operator in lattice QCD. Numerische Mathematik 132(3): 463-490 (2016) - 2014
- [j1]Andreas Frommer, Karsten Kahl, Stefan Krieg, Björn Leder, Matthias Rottmann:
Adaptive Aggregation-Based Domain Decomposition Multigrid for the Lattice Wilson-Dirac Operator. SIAM J. Sci. Comput. 36(4) (2014)
Coauthor Index
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