| Quicksilver: Fast predictive image registration–a deep learning approach X Yang, R Kwitt, M Styner, M Niethammer NeuroImage 158, 378-396, 2017 | 727 | 2017 |
| A stable multi-scale kernel for topological machine learning J Reininghaus, S Huber, U Bauer, R Kwitt Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 453 | 2015 |
| BlenSor: Blender sensor simulation toolbox M Gschwandtner, R Kwitt, A Uhl, W Pree Advances in Visual Computing: 7th International Symposium, ISVC 2011, Las …, 2011 | 390 | 2011 |
| Deep learning with topological signatures C Hofer, R Kwitt, M Niethammer, A Uhl Advances in neural information processing systems 30, 2017 | 326 | 2017 |
| Fast predictive image registration X Yang, R Kwitt, M Niethammer Deep Learning and Data Labeling for Medical Applications: First …, 2016 | 178 | 2016 |
| Dissecting Supervised Contrastive Learning F Graf, C Hofer, M Niethammer, R Kwitt International Conference on Machine Learning, 3821-3830, 2021 | 158 | 2021 |
| Aga: Attribute-guided augmentation M Dixit, R Kwitt, M Niethammer, N Vasconcelos Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 156 | 2017 |
| Feature space transfer for data augmentation B Liu, X Wang, M Dixit, R Kwitt, N Vasconcelos Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 123 | 2018 |
| Scene recognition on the semantic manifold R Kwitt, N Vasconcelos, N Rasiwasia European Conference on Computer Vision (ECCV 2012), 359-372, 2012 | 120 | 2012 |
| Lightweight probabilistic texture retrieval R Kwitt, A Uhl IEEE Transactions on Image Processing 19 (1), 241-253, 2009 | 120 | 2009 |
| Graph filtration learning C Hofer, F Graf, B Rieck, M Niethammer, R Kwitt International Conference on Machine Learning, 4314-4323, 2020 | 119 | 2020 |
| Metric learning for image registration M Niethammer, R Kwitt, FX Vialard Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 108 | 2019 |
| Statistical topological data analysis-a kernel perspective R Kwitt, S Huber, M Niethammer, W Lin, U Bauer Advances in neural information processing systems 28, 2015 | 107 | 2015 |
| Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval R Kwitt, A Uhl 2008 15th IEEE International Conference on Image Processing, 933-936, 2008 | 97 | 2008 |
| Connectivity-optimized representation learning via persistent homology C Hofer, R Kwitt, M Niethammer, M Dixit International conference on machine learning, 2751-2760, 2019 | 93 | 2019 |
| Learning representations of persistence barcodes CD Hofer, R Kwitt, M Niethammer Journal of Machine Learning Research 20 (126), 1-45, 2019 | 89 | 2019 |
| Efficient texture image retrieval using copulas in a Bayesian framework R Kwitt, P Meerwald, A Uhl IEEE transactions on image processing 20 (7), 2063-2077, 2011 | 88 | 2011 |
| Lightweight detection of additive watermarking in the DWT-domain R Kwitt, P Meerwald, A Uhl IEEE transactions on image processing 20 (2), 474-484, 2010 | 78 | 2010 |
| Stochastic block models with multiple continuous attributes N Stanley, T Bonacci, R Kwitt, M Niethammer, PJ Mucha Applied Network Science 4, 1-22, 2019 | 77 | 2019 |
| One-shot learning of scene locations via feature trajectory transfer R Kwitt, S Hegenbart, M Niethammer Proceedings of The IEEE conference on computer vision and pattern …, 2016 | 69 | 2016 |
Marc NiethammerProfessor of Computer Science, UC San Diegocs.unc.edu의 이메일 확인됨