Adaptive Aggregation-Based Domain Decomposition Multigrid for the Lattice Wilson-Dirac Operator. - Andreas Frommer; Karsten Kahl; Stefan Krieg; Björn Leder; Matthias Rottmann (SIAM J. Sci. Comput., 2014)

Multigrid preconditioning for the overlap operator in lattice QCD. - James J. Brannick; Andreas Frommer; Karsten Kahl; Björn Leder; Matthias Rottmann; Artur Strebel (Numerische Mathematik, 2016)

Classification Uncertainty of Deep Neural Networks Based on Gradient Information. - Philipp Oberdiek; Matthias Rottmann; Hanno Gottschalk (ANNPR, 2018)

Deep Bayesian Active Semi-Supervised Learning. - Matthias Rottmann; Karsten Kahl; Hanno Gottschalk (ICMLA, 2018)

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. - Matthias Rottmann; Pascal Colling; Thomas-Paul Hack; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2018)

Least Angle Regression Coarsening in Bootstrap Algebraic Multigrid. - Karsten Kahl; Matthias Rottmann (SIAM J. Sci. Comput., 2018)

The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Radin Dardashti; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CVPR Workshops, 2019)

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

Period Index: A Learned 2D Hash Index for Range and Duration Queries. - Andreas Behrend; Anton Dignös; Johann Gamper; Philip Schmiegelt; Hannes Voigt; Matthias Rottmann; Karsten Kahl (SSTD, 2019)

Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2019)

MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2019)

Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation. - Philipp Oberdiek; Matthias Rottmann; Gernot A. Fink (CVPR Workshops, 2020)

Detection of False Positive and False Negative Samples in Semantic Segmentation. - Matthias Rottmann; Kira Maag; Robin Chan; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (DATE, 2020)

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. - Kira Maag; Matthias Rottmann; Hanno Gottschalk (ICTAI, 2020)

Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2020)

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. - Matthias Rottmann; Pascal Colling; Thomas-Paul Hack; Robin Chan; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2020)

Detection of Iterative Adversarial Attacks via Counter Attack. - Matthias Rottmann; Mathis Peyron; Natasa Krejic; Hanno Gottschalk (CoRR, 2020)

A Convenient Infinite Dimensional Framework for Generative Adversarial Learning. - Hayk Asatryan; Hanno Gottschalk; Marieke Lippert; Matthias Rottmann (CoRR, 2020)

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. - Kira Maag; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2020)

YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors. - Kamil Kowol; Matthias Rottmann; Stefan Bracke; Hanno Gottschalk (ICAART (2), 2021)

Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Hanno Gottschalk (ICCV, 2021)

MetaBox+: A New Region based Active Learning Method for Semantic Segmentation using Priority Maps. - Pascal Colling; Lutz Roese-Koerner; Hanno Gottschalk; Matthias Rottmann (ICPRAM, 2021)

False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation. - Pascal Colling; Matthias Rottmann; Lutz Roese-Koerner; Hanno Gottschalk (ICTAI, 2021)

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. - Kira Maag; Matthias Rottmann; Serin Varghese; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2021)

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

Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis. - 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 (IV Workshops, 2021)

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. - Robin Chan; Krzysztof Lis; Svenja Uhlemeyer; Hermann Blum; Sina Honari; Roland Siegwart; Pascal Fua; Mathieu Salzmann; Matthias Rottmann (NeurIPS Datasets and Benchmarks, 2021)

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. - 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; et al. (CoRR, 2021)

Background-Foreground Segmentation for Interior Sensing in Automotive Industry. - Claudia Drygala; Matthias Rottmann; Hanno Gottschalk; Klaus Friedrichs; Thomas Kurbiel (CoRR, 2021)

Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance? - Hanno Gottschalk; Matthias Rottmann; Maida Saltagic (CoRR, 2021)

A multigrid accelerated eigensolver for the Hermitian Wilson-Dirac operator in lattice QCD. - Andreas Frommer; Karsten Kahl; Francesco Knechtli; Matthias Rottmann; Artur Strebel; Ian Zwaan (Comput. Phys. Commun., 2021)

HD Lane Map Generation Based on Trail Map Aggregation. - Pascal Colling; Dennis Müller 004; Matthias Rottmann (IV, 2022)

UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs. - Philipp Oberdiek; Gernot A. Fink; Matthias Rottmann (NeurIPS, 2022)

Towards unsupervised open world semantic segmentation. - Svenja Uhlemeyer; Matthias Rottmann; Hanno Gottschalk (UAI, 2022)

Detecting and Learning the Unknown in Semantic Segmentation. - Robin Chan; Svenja Uhlemeyer; Matthias Rottmann; Hanno Gottschalk (CoRR, 2022)

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)

Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss. - Annika Mütze; Matthias Rottmann; Hanno Gottschalk (CoRR, 2022)

MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions. - Antonia van Betteray; Matthias Rottmann; Karsten Kahl (CoRR, 2022)

AttEntropy: Segmenting Unknown Objects in Complex Scenes using the Spatial Attention Entropy of Semantic Segmentation Transformers. - Krzysztof Lis; Matthias Rottmann; Sina Honari; Pascal Fua; Mathieu Salzmann (CoRR, 2022)

MGiaD: Multigrid in all dimensions. Efficiency and robustness by weight sharing and coarsening in resolution and channel dimensions. - Antonia van Betteray; Matthias Rottmann; Karsten Kahl (ICCV (Workshops), 2023)

False Negative Reduction in Semantic Segmentation Under Domain Shift Using Depth Estimation. - Kira Maag; Matthias Rottmann (VISIGRAPP (5: VISAPP), 2023)

Semi-Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness. - Annika Mütze; Matthias Rottmann; Hanno Gottschalk (VISIGRAPP (5: VISAPP), 2023)

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. - Tobias Riedlinger; Matthias Rottmann; Marius Schubert; Hanno Gottschalk (WACV, 2023)

Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification. - Matthias Rottmann; Marco Reese (WACV, 2023)

Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations. - Lorenc Kapllani; Long Teng 002; Matthias Rottmann (CoRR, 2023)

Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds. - Pascal Colling; Matthias Rottmann; Lutz Roese-Koerner; Hanno Gottschalk (Int. J. Artif. Intell. Tools, 2023)

Detection of Iterative Adversarial Attacks via Counter Attack. - Matthias Rottmann; Kira Maag; Mathis Peyron; Hanno Gottschalk; Natasa Krejic (J. Optim. Theory Appl., 2023)

What should AI see? Using the public's opinion to determine the perception of an AI. - Robin Chan; Radin Dardashti; Meike Osinski; Matthias Rottmann; Dominik Brüggemann; Cilia Rücker; Peter Schlicht; Fabian Hüger; Nikol Rummel; Hanno Gottschalk (AI Ethics, 2023)

Coarsest-level improvements in multigrid for lattice QCD on large-scale computers. - Jesus Espinoza-Valverde; Andreas Frommer; Gustavo Ramirez-Hidalgo; Matthias Rottmann (Comput. Phys. Commun., 2023)

AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains. - Krzysztof Baron-Lis; Matthias Rottmann; Annika Mütze; Sina Honari; Pascal Fua; Mathieu Salzmann (BMVC, 2024)

Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Network. - Edgar Heinert; Stephan Tilgner; Timo Palm; Matthias Rottmann (BMVC Workshops, 2024)

Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion. - Edgar Heinert; Matthias Rottmann; Kira Maag; Karsten Kahl (ECAI, 2024)

On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenes. - Sadia Ilyas; Ido Freeman; Matthias Rottmann (ECCV Workshops (18), 2024)

SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data. - Jialong Wu 008; Mirko Meuter; Markus Schoeler; Matthias Rottmann (ECCV (86), 2024)

ResBuilder: Automated Learning of Depth with Residual Structures. - Julian Burghoff; Matthias Rottmann; Jill von Conta; Sebastian Schoenen; Andreas Witte; Hanno Gottschalk (ICANN (1), 2024)

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)

OoDIS: Anomaly Instance Segmentation Benchmark. - Alexey Nekrasov 001; Rui Zhou; Miriam Ackermann; Alexander Hermans; Bastian Leibe; Matthias Rottmann (CoRR, 2024)

Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks. - Edgar Heinert; Stephan Tilgner; Timo Palm; Matthias Rottmann (CoRR, 2024)

Unveiling Ontological Commitment in Multi-Modal Foundation Models. - Mert Keser; Gesina Schwalbe; Niki Amini-Naieni; Matthias Rottmann; Alois Knoll (CoRR, 2024)

On the Influence of Shape, Texture and Color for Learning Semantic Segmentation. - Annika Mütze; Natalie Grabowsky; Edgar Heinert; Matthias Rottmann; Hanno Gottschalk (CoRR, 2024)

Poly-MgNet: Polynomial Building Blocks in Multigrid-Inspired ResNets. - Antonia van Betteray; Matthias Rottmann; Karsten Kahl (ICPRAM, 2025)

Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving. - Mert Keser; Halil Ibrahim Orhan; Niki Amini-Naieni; Gesina Schwalbe; Alois Knoll; Matthias Rottmann (CoRR, 2025)

Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving? - Natalie Grabowsky; Annika Mütze; Joshua Wendland; Nils Jansen 001; Matthias Rottmann (CoRR, 2025)

TARS: Traffic-Aware Radar Scene Flow Estimation. - Jialong Wu 008; Marco Braun; Dominic Spata; Matthias Rottmann (CoRR, 2025)

Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation. - Edgar Heinert; Thomas Gottwald; Annika Mütze; Matthias Rottmann (CoRR, 2025)

On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs. - Gesina Schwalbe; Georgii Mikriukov; Edgar Heinert; Stavros Gerolymatos; Mert Keser; Alois Knoll; Matthias Rottmann; Annika Mütze (CoRR, 2025)

LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking. - Martha Teiko Teye; Ori Maoz; Matthias Rottmann (CoRR, 2025)

Temporal Object Captioning for Street Scene Videos from LiDAR Tracks. - Vignesh Gopinathan; Urs Zimmermann; Michael Arnold; Matthias Rottmann (CoRR, 2025)

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis. - Antonia van Betteray; Matthias Rottmann; Karsten Kahl (CoRR, 2025)

Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes? - Annika Mütze; Sadia Ilyas; Christian Dörpelkus; Matthias Rottmann (CoRR, 2025)

Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks. - Ben Hamscher; Edgar Heinert; Annika Mütze; Kira Maag; Matthias Rottmann (CoRR, 2025)

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)