| 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 | 1881 | 2016 |
| Self-labelling via simultaneous clustering and representation learning YM Asano, C Rupprecht, A Vedaldi arXiv preprint arXiv:1911.05371, 2019 | 539 | 2019 |
| 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 | 241 | 2020 |
| Robust Optimization for Deep Regression V Belagiannis, C Rupprecht, G Carneiro, N Navab Proceedings of the IEEE International Conference on Computer Vision, 2015 | 198 | 2015 |
| 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 | 164 | 2017 |
| Concurrent segmentation and localization for tracking of surgical instruments I Laina, N Rieke, C Rupprecht, JP Vizcaíno, A Eslami, F Tombari, ... Medical Image Computing and Computer-Assisted Intervention− MICCAI 2017 …, 2017 | 155 | 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), 2049, 2017 | 151 | 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 | 137 | 2020 |
| 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 | 108 | 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 | 97 | 2016 |
| 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 | 89 | 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 | 82 | 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 | 75 | 2020 |
| Deep Active Contours C Rupprecht, E Huaroc, M Baust, N Navab arXiv preprint arXiv:1607.05074, 2016 | 75 | 2016 |
| Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization L Melas-Kyriazi, C Rupprecht, I Laina, A Vedaldi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 42 | 2022 |
| 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), 7569, 2019 | 39 | 2019 |
| 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 | 35 | 2018 |
| Image segmentation in twenty questions C Rupprecht, L Peter, N Navab Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 35 | 2015 |
| ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation L Karazija, I Laina, C Rupprecht Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 34 | 2021 |
| Unsupervised part discovery from contrastive reconstruction S Choudhury, I Laina, C Rupprecht, A Vedaldi Advances in Neural Information Processing Systems 34, 28104-28118, 2021 | 32 | 2021 |
Nassir NavabProfessor of Computer Science, Technische Universität MünchenVerified email at cs.tum.edu
