| Deeper Depth Prediction with Fully Convolutional Residual Networks I Laina, C Rupprecht, V Belagiannis, F Tombari, N Navab 3D Vision (3DV), 2016 Fourth International Conference on, 239-248, 2016 | 1522 | 2016 |
| Self-labelling via simultaneous clustering and representation learning YM Asano, C Rupprecht, A Vedaldi arXiv preprint arXiv:1911.05371, 2019 | 291 | 2019 |
| Robust Optimization for Deep Regression V Belagiannis, C Rupprecht, G Carneiro, N Navab Proceedings of the IEEE International Conference on Computer Vision, 2015 | 181 | 2015 |
| Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild S Wu, C Rupprecht, A Vedaldi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 132 | 2020 |
| Concurrent segmentation and localization for tracking of surgical instruments I Laina, N Rieke, C Rupprecht, JP Vizcaíno, A Eslami, F Tombari, ... International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 132 | 2017 |
| Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease K Sharma, C Rupprecht, A Caroli, MC Aparicio, A Remuzzi, M Baust, ... Scientific reports 7 (1), 1-10, 2017 | 124 | 2017 |
| Learning in an uncertain world: Representing ambiguity through multiple hypotheses C Rupprecht, I Laina, R DiPietro, M Baust, F Tombari, N Navab, GD Hager International Conference on Computer Vision (ICCV), 2017 | 121 | 2017 |
| A critical analysis of self-supervision, or what we can learn from a single image YM Asano, C Rupprecht, A Vedaldi International Conference on Learning Representations, 2020 | 99* | 2020 |
| A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks F Grün, C Rupprecht, N Navab, F Tombari arXiv preprint arXiv:1606.07757, 2016 | 85 | 2016 |
| Deep Active Contours C Rupprecht, E Huaroc, M Baust, N Navab arXiv preprint arXiv:1607.05074, 2016 | 64 | 2016 |
| Labelling unlabelled videos from scratch with multi-modal self-supervision Y Asano, M Patrick, C Rupprecht, A Vedaldi Advances in Neural Information Processing Systems 33, 2020 | 53 | 2020 |
| Explaining the ambiguity of object detection and 6d pose from visual data F Manhardt, DM Arroyo, C Rupprecht, B Busam, T Birdal, N Navab, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 49 | 2019 |
| Towards unsupervised image captioning with shared multimodal embeddings I Laina, C Rupprecht, N Navab Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 42 | 2019 |
| Semantic Image Manipulation Using Scene Graphs H Dhamo, A Farshad, I Laina, N Navab, GD Hager, F Tombari, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 36 | 2020 |
| Image segmentation in twenty questions C Rupprecht, L Peter, N Navab Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 34 | 2015 |
| Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies R DiPietro, C Rupprecht, N Navab, GD Hager arXiv preprint arXiv:1702.07805, 2017 | 26 | 2017 |
| Guide me: Interacting with deep networks C Rupprecht, I Laina, N Navab, GD Hager, F Tombari Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 25 | 2018 |
| Sensor Substitution for Video-based Action Recognition C Rupprecht, C Lea, F Tombari, N Navab, GD Hager IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), 2016 | 23 | 2016 |
| Classification of polar maps from cardiac perfusion imaging with graph-convolutional neural networks N Spier, S Nekolla, C Rupprecht, M Mustafa, N Navab, M Baust Scientific reports 9 (1), 1-8, 2019 | 20 | 2019 |
| Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents C Rupprecht, C Ibrahim, CJ Pal arXiv preprint arXiv:1904.01318, 2019 | 20 | 2019 |
Nassir NavabProfessor of Computer Science, Technische Universität MünchenVerified email at cs.tum.edu
