| Flownet 2.0: Evolution of optical flow estimation with deep networks E Ilg, N Mayer, T Saikia, M Keuper, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 3689 | 2017 |
| Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions R Durall, M Keuper, J Keuper Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 322 | 2020 |
| Unmasking deepfakes with simple features R Durall, M Keuper, FJ Pfreundt, J Keuper arXiv preprint arXiv:1911.00686, 2019 | 248 | 2019 |
| Motion segmentation & multiple object tracking by correlation co-clustering M Keuper, S Tang, B Andres, T Brox, B Schiele IEEE transactions on pattern analysis and machine intelligence 42 (1), 140-153, 2018 | 244 | 2018 |
| Motion trajectory segmentation via minimum cost multicuts M Keuper, B Andres, T Brox Proceedings of the IEEE international conference on computer vision, 3271-3279, 2015 | 235 | 2015 |
| Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation E Ilg, T Saikia, M Keuper, T Brox Proceedings of the European conference on computer vision (ECCV), 614-630, 2018 | 232 | 2018 |
| Nas-bench-301 and the case for surrogate benchmarks for neural architecture search J Siems, L Zimmer, A Zela, J Lukasik, M Keuper, F Hutter arXiv preprint arXiv:2008.09777 4, 14, 2020 | 176 | 2020 |
| Efficient decomposition of image and mesh graphs by lifted multicuts M Keuper, E Levinkov, N Bonneel, G Lavoué, T Brox, B Andres Proceedings of the IEEE international conference on computer vision, 1751-1759, 2015 | 149 | 2015 |
| Std2p: Rgbd semantic segmentation using spatio-temporal data-driven pooling Y He, WC Chiu, M Keuper, M Fritz Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 146 | 2017 |
| 3D rotation invariant local binary patterns J Fehr, H Burkhardt 2008 19th International conference on pattern recognition, 1-4, 2008 | 132 | 2008 |
| Distributed training of deep neural networks: Theoretical and practical limits of parallel scalability J Keuper, FJ Preundt 2016 2nd workshop on machine learning in HPC environments (MLHPC), 19-26, 2016 | 117 | 2016 |
| A multi-cut formulation for joint segmentation and tracking of multiple objects M Keuper, S Tang, Y Zhongjie, B Andres, T Brox, B Schiele arXiv preprint arXiv:1607.06317, 2016 | 111 | 2016 |
| Spectral graph reduction for efficient image and streaming video segmentation F Galasso, M Keuper, T Brox, B Schiele Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 104 | 2014 |
| Generating EPI representations of 4D light fields with a single lens focused plenoptic camera S Wanner, J Fehr, B Jähne International Symposium on Visual Computing, 90-101, 2011 | 98 | 2011 |
| Extracting horizon surfaces from 3D seismic data using deep learning V Tschannen, M Delescluse, N Ettrich, J Keuper Geophysics 85 (3), N17-N26, 2020 | 86 | 2020 |
| Computing Range Flow from Multi-modal Kinect Data JM Gottfried, J Fehr, CS Garbe Advances in Visual Computing: 7th International Symposium, ISVC 2011, Las …, 2011 | 82 | 2011 |
| Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks A Zela, J Siems, L Zimmer, J Lukasik, M Keuper, F Hutter arXiv preprint arXiv:2008.09777, 2020 | 66 | 2020 |
| Probflow: Joint optical flow and uncertainty estimation AS Wannenwetsch, M Keuper, S Roth Proceedings of the IEEE international conference on computer vision, 1173-1182, 2017 | 64 | 2017 |
| Beyond the spectrum: Detecting deepfakes via re-synthesis Y He, N Yu, M Keuper, M Fritz arXiv preprint arXiv:2105.14376, 2021 | 54 | 2021 |
| Combating mode collapse in gan training: An empirical analysis using hessian eigenvalues R Durall, A Chatzimichailidis, P Labus, J Keuper arXiv preprint arXiv:2012.09673, 2020 | 52 | 2020 |
