| 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 | 2354 | 2016 |
| Self-labelling via simultaneous clustering and representation learning YM Asano, C Rupprecht, A Vedaldi arXiv preprint arXiv:1911.05371, 2019 | 891 | 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 | 360 | 2020 |
| RealFusion: 360deg Reconstruction of Any Object From a Single Image L Melas-Kyriazi, I Laina, C Rupprecht, A Vedaldi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 279 | 2023 |
| 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 | 229 | 2017 |
| Robust Optimization for Deep Regression V Belagiannis, C Rupprecht, G Carneiro, N Navab Proceedings of the IEEE International Conference on Computer Vision, 2015 | 222 | 2015 |
| 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 | 201 | 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 | 191 | 2017 |
| 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 | 178 | 2020 |
| 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 | 170 | 2020 |
| Cotracker: It is better to track together N Karaev, I Rocco, B Graham, N Neverova, A Vedaldi, C Rupprecht European Conference on Computer Vision, 18-35, 2025 | 166 | 2025 |
| 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 | 161 | 2022 |
| 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 | 131 | 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 | 130 | 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 | 125 | 2019 |
| Splatter image: Ultra-fast single-view 3d reconstruction S Szymanowicz, C Rupprecht, A Vedaldi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 108 | 2024 |
| What does clip know about a red circle? visual prompt engineering for vlms A Shtedritski, C Rupprecht, A Vedaldi Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 108 | 2023 |
| 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 | 108 | 2016 |
| Deep Active Contours C Rupprecht, E Huaroc, M Baust, N Navab arXiv preprint arXiv:1607.05074, 2016 | 90 | 2016 |
| Viewset diffusion:(0-) image-conditioned 3d generative models from 2d data S Szymanowicz, C Rupprecht, A Vedaldi Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 77 | 2023 |
Andrea VedaldiUniversity of Oxford在 robots.ox.ac.uk 的电子邮件经过验证
