| Fully-convolutional siamese networks for object tracking L Bertinetto, J Valmadre, JF Henriques, A Vedaldi, PHS Torr Proceedings of the European Conference on Computer Vision (ECCV) Workshops …, 2016 | 4969 | 2016 |
| Staple: Complementary Learners for Real-Time Tracking L Bertinetto, J Valmadre, S Golodetz, O Miksik, P Torr CVPR - The IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 2122 | 2016 |
| End-to-end representation learning for the Correlation Filter J Valmadre, L Bertinetto, JF Henriques, A Vedaldi, PHS Torr CVPR - The IEEE Conference on Computer Vision and Pattern Recognition, 2017 | 1836* | 2017 |
| Fast online object tracking and segmentation: A unifying approach Q Wang, L Zhang, L Bertinetto, W Hu, PHS Torr CVPR - Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 1621 | 2019 |
| Meta-learning with differentiable closed-form solvers L Bertinetto, JF Henriques, PHS Torr, A Vedaldi ICLR - Proceedings of the 7th International Conference on Learning …, 2019 | 1114 | 2019 |
| The sixth visual object tracking vot2018 challenge results M Kristan, A Leonardis, J Matas, M Felsberg, R Pflugfelder, ... Proceedings of the European conference on computer vision (ECCV) workshops, 0-0, 2018 | 964 | 2018 |
| Learning feed-forward one-shot learners L Bertinetto, JF Henriques, J Valmadre, P Torr, A Vedaldi NeurIPS - Advances in Neural Information Processing Systems, 523-531, 2016 | 547 | 2016 |
| The eighth visual object tracking VOT2020 challenge results M Kristan, A Leonardis, J Matas, M Felsberg, R Pflugfelder, ... Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 323 | 2020 |
| Long-term Tracking in the Wild: A Benchmark J Valmadre, L Bertinetto, JF Henriques, R Tao, A Vedaldi, A Smeulders, ... ECCV - The European Conference on Computer Vision, 2018 | 193 | 2018 |
| The visual object tracking vot2016 challenge results G Roffo, S Melzi Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 142 | 2016 |
| Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks L Bertinetto, R Mueller, K Tertikas, S Samangooei, NA Lord CVPR - The IEEE Conference on Computer Vision and Pattern Recognition, 2020 | 138 | 2020 |
| Anchor diffusion for unsupervised video object segmentation Z Yang, Q Wang, L Bertinetto, W Hu, S Bai, PHS Torr ICCV - Proceedings of the IEEE/CVF International Conference on Computer …, 2019 | 131 | 2019 |
| Parameter-free Online Test-time Adaptation M Boudiaf, R Mueller, IB Ayed, L Bertinetto CVPR - The IEEE Conference on Computer Vision and Pattern Recognition, 2022 | 126 | 2022 |
| On episodes, prototypical networks, and few-shot learning S Laenen, L Bertinetto NeurIPS - Advances in Neural Information Processing Systems, 2021 | 106 | 2021 |
| Do Different Tracking Tasks Require Different Appearance Models? Z Wang, H Zhao, YL Li, S Wang, PHS Torr, L Bertinetto NeurIPS - Advances in Neural Information Processing Systems, 2021 | 86 | 2021 |
| SiamMask: A Framework for Fast Online Object Tracking and Segmentation W Hu, Q Wang, L Zhang, L Bertinetto, PHS Torr TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 65 | 2022 |
| Let's take this online: Adapting scene coordinate regression network predictions for online RGB-D camera relocalisation T Cavallari, L Bertinetto, J Mukhoti, P Torr, S Golodetz 3DV - International Conference on 3D Vision, 564-573, 2019 | 42 | 2019 |
| Self-supervised test-time adaptation on video data F Azimi, S Palacio, F Raue, J Hees, L Bertinetto, A Dengel WACV - Proceedings of the IEEE/CVF Winter Conference on Applications of …, 2022 | 28 | 2022 |
| Attacking deep networks with surrogate-based adversarial black-box methods is easy NA Lord, R Mueller, L Bertinetto ICLR - Proceedings of the 10th International Conference on Learning …, 2022 | 22 | 2022 |
| The Importance of Estimating Object Extent when Tracking with Correlation Filters L Bertinetto, J Valmadre, O Miksik, S Golodetz, P Torr Technical report, 2015 | 7 | 2015 |
Philip TorrProfessor, University of Oxford在 eng.ox.ac.uk 的電子郵件地址已通過驗證