Следене
Luca Bertinetto
Luca Bertinetto
Exscientia, University of Oxford
Потвърден имейл адрес: exscientia.co.uk - Начална страница
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Позовавания
Позовавания
Година
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
46432016
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
20532016
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
1774*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
15092019
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
9992019
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
9132018
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
5222016
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
2832020
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
1902018
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
1242019
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
1152020
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
942022
On episodes, prototypical networks, and few-shot learning
S Laenen, L Bertinetto
NeurIPS - Advances in Neural Information Processing Systems, 2021
902021
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
722021
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
392019
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
382022
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
252022
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
162022
Hierarchical interaction network for video object segmentation from referring expressions
Z Yang, Y Tang, L Bertinetto, H Zhao, PHS Torr
BMVC - Proceedings of the British Machine Vision Conference, 2021
152021
The Importance of Estimating Object Extent when Tracking with Correlation Filters
L Bertinetto, J Valmadre, O Miksik, S Golodetz, P Torr
Technical report, 2015
72015
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