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Jack Valmadre
Jack Valmadre
Senior Research Fellow, AIML, University of Adelaide
Zweryfikowany adres z adelaide.edu.au - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Fully-convolutional siamese networks for object tracking
L Bertinetto, J Valmadre, JF Henriques, A Vedaldi, PHS Torr
Computer Vision – ECCV 2016 Workshops, 850-865, 2016
43162016
Staple: Complementary learners for real-time tracking
L Bertinetto, J Valmadre, S Golodetz, O Miksik, PHS Torr
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1401-1409, 2016
20042016
End-to-end representation learning for correlation filter based tracking
J Valmadre, L Bertinetto, J Henriques, A Vedaldi, PHS Torr
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2805-2813, 2017
17222017
Learning feed-forward one-shot learners
L Bertinetto, JF Henriques, J Valmadre, PHS Torr, A Vedaldi
Advances in Neural Information Processing Systems (NeurIPS), 523-531, 2016
5012016
Long-term tracking in the wild: A benchmark
J Valmadre, L Bertinetto, JF Henriques, R Tao, A Vedaldi, ...
European Conference on Computer Vision (ECCV), 670-685, 2018
1822018
Dense semantic correspondence where every pixel is a classifier
H Bristow, J Valmadre, S Lucey
IEEE International Conference on Computer Vision (ICCV), 4024-4031, 2015
662015
General trajectory prior for non-rigid reconstruction
J Valmadre, S Lucey
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1394-1401, 2012
642012
Deterministic 3D human pose estimation using rigid structure
J Valmadre, S Lucey
European Conference on Computer Vision (ECCV), 467-480, 2010
582010
Learning with Neighbor Consistency for Noisy Labels
A Iscen, J Valmadre, A Arnab, C Schmid
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4672-4681, 2022
372022
Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds
T Simon, J Valmadre, I Matthews, Y Sheikh
European Conference on Computer Vision (ECCV), 204-219, 2014
372014
Devon: Deformable volume network for learning optical flow
Y Lu, J Valmadre, H Wang, J Kannala, M Harandi, P Torr
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2705-2713, 2020
362020
Efficient articulated trajectory reconstruction using dynamic programming and filters
J Valmadre, Y Zhu, S Sridharan, S Lucey
European Conference on Computer Vision (ECCV), 72–85, 2012
202012
Kronecker-markov prior for dynamic 3d reconstruction
T Simon, J Valmadre, I Matthews, Y Sheikh
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 39 (11 …, 2016
182016
Local metrics for multi-object tracking
J Valmadre, A Bewley, J Huang, C Sun, C Sminchisescu, C Schmid
arXiv preprint arXiv:2104.02631, 2021
132021
Learning detectors quickly with stationary statistics
J Valmadre, S Sridharan, S Lucey
Asian Conference on Computer Vision (ACCV), 99-114, 2014
13*2014
and Z. Chi. The visual object tracking vot2016 challenge results
M Kristan, A Leonardis, J Matas
Computer vision–ECCV, 8-10, 2016
72016
Closed-form solutions for low-rank non-rigid reconstruction
J Valmadre, S Sridharan, S Denman, C Fookes, S Lucey
International Conference on Digital Image Computing: Techniques and …, 2015
72015
The importance of estimating object extent when tracking with correlation filters
L Bertinetto, J Valmadre, S Golodetz, O Miksik, PHS Torr
ICCV VOT workshop, 2015
72015
Camera-less articulated trajectory reconstruction
Y Zhu, J Valmadre, S Lucey
21st International Conference on Pattern Recognition (ICPR), 2012
62012
Hierarchical classification at multiple operating points
J Valmadre
Advances in Neural Information Processing Systems 35, 18034-18045, 2022
32022
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