| Automatically discovering and learning new visual categories with ranking statistics K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman arXiv preprint arXiv:2002.05714, 2020 | 183 | 2020 |
| Autonovel: Automatically discovering and learning novel visual categories K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6767 …, 2021 | 95 | 2021 |
| 3d multi-bodies: Fitting sets of plausible 3d human models to ambiguous image data B Biggs, D Novotny, S Ehrhardt, H Joo, B Graham, A Vedaldi Advances in neural information processing systems 33, 20496-20507, 2020 | 70 | 2020 |
| Co-attention for conditioned image matching O Wiles, S Ehrhardt, A Zisserman Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 59* | 2021 |
| RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces S Ehrhardt, O Groth, A Monszpart, M Engelcke, I Posner, N Mitra, ... Advances in Neural Information Processing Systems 33, 2020 | 53 | 2020 |
| Semi-supervised learning with scarce annotations SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 52 | 2020 |
| Learning a physical long-term predictor S Ehrhardt, A Monszpart, NJ Mitra, A Vedaldi arXiv preprint arXiv:1703.00247, 2017 | 50 | 2017 |
| Lsd-c: Linearly separable deep clusters SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 27 | 2021 |
| Unsupervised intuitive physics from visual observations S Ehrhardt, A Monszpart, N Mitra, A Vedaldi Asian Conference on Computer Vision, 700-716, 2018 | 26 | 2018 |
| Taking visual motion prediction to new heightfields S Ehrhardt, A Monszpart, NJ Mitra, A Vedaldi Computer Vision and Image Understanding 181, 14-25, 2019 | 21 | 2019 |
| Small steps and giant leaps: Minimal newton solvers for deep learning JF Henriques, S Ehrhardt, S Albanie, A Vedaldi Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 19 | 2019 |
| Learning to represent mechanics via long-term extrapolation and interpolation S Ehrhardt, A Monszpart, A Vedaldi, N Mitra arXiv preprint arXiv:1706.02179, 2017 | 10 | 2017 |
| Unsupervised intuitive physics from past experiences S Ehrhardt, A Monszpart, NJ Mitra, A Vedaldi arXiv preprint arXiv:1905.10793, 2019 | 6 | 2019 |
| Stopping gan violence: Generative unadversarial networks S Albanie, S Ehrhardt, JF Henriques arXiv preprint arXiv:1703.02528, 2017 | 6 | 2017 |
| Deep industrial espionage S Albanie, J Thewlis, S Ehrhardt, J Henriques arXiv preprint arXiv:1904.01114, 2019 | 3 | 2019 |
| Learning visual concepts with fewer human annotations S Ehrhardt University of Oxford, 2020 | | 2020 |
| LSD-C: Linearly Separable Deep Clusters–Supplementary Material– SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman | | |
| How does mini-batching affect curvature information for second order deep learning optimization? D Granziol, X Wan, S Zohren, S Roberts, T Garipov, D Vetrov, AG Wilson, ... | | |
Andrea VedaldiUniversity of OxfordEmail được xác minh tại robots.ox.ac.uk
