Jack Valmadre

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Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
Luca BertinettoExscientia, University of OxfordVerified email at robots.ox.ac.uk
Andrea VedaldiUniversity of OxfordVerified email at robots.ox.ac.uk
João F. HenriquesVisual Geometry Group, University of OxfordVerified email at robots.ox.ac.uk
Simon LuceyProfessor, University of AdelaideVerified email at adelaide.edu
Stuart GolodetzUniversity of OxfordVerified email at cs.ox.ac.uk
Ondrej MiksikMicrosoft Mixed Reality & AI Lab ZurichVerified email at robots.ox.ac.uk
Sridha SridharanProfessorVerified email at qut.edu.au
Ran TaoUniversity of AmsterdamVerified email at uva.nl
Arnold SmeuldersProfessor of Computer Science, University of AmsterdamVerified email at uva.nl
Efstratios GavvesAssociate Professor at University of AmsterdamVerified email at uva.nl
Yaser SheikhDirector, Facebook Reality Labs (Pittsburgh)Verified email at cs.cmu.edu
Tomas SimonPh.D. student, The Robotics Institute, Carnegie Mellon UniversityVerified email at cmu.edu
Iain MatthewsEpic Games / University of East Anglia / Carnegie Mellon UniversityVerified email at uea.ac.uk
Hilton BristowCSIRO, Queensland University of TechnologyVerified email at csiro.au
Yingying ZhuUniversity of Texas at Arlington Assistant ProfessorVerified email at uta.edu
Yao LuPeking UniversityVerified email at pku.edu.cn
Mehrtash HarandiDepartment of Electrical and Computer Systems Engineering, Monash UniversityVerified email at monash.edu
Heng WangFacebook AI ResearchVerified email at fb.com
Juho KannalaAssistant Professor, Aalto University, FinlandVerified email at aalto.fi

Jack Valmadre

Australian Institute of Machine Learning, University of Adelaide

Verified email at adelaide.edu.au - Homepage

Computer vision Machine learning Deep learning


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Fully-convolutional siamese networks for object tracking L Bertinetto, J Valmadre, JF Henriques, A Vedaldi, PHS Torr European conference on computer vision, 850-865, 2016	2991	2016
Staple: Complementary learners for real-time tracking L Bertinetto, J Valmadre, S Golodetz, O Miksik, PHS Torr Proceedings of the IEEE conference on computer vision and pattern …, 2016	1650	2016
End-to-end representation learning for correlation filter based tracking J Valmadre, L Bertinetto, J Henriques, A Vedaldi, PHS Torr Proceedings of the IEEE conference on computer vision and pattern …, 2017	1362	2017
Learning feed-forward one-shot learners L Bertinetto, JF Henriques, J Valmadre, PHS Torr, A Vedaldi Advances in Neural Information Processing Systems, 523-531, 2016	406	2016
Long-term tracking in the wild: A benchmark J Valmadre, L Bertinetto, JF Henriques, R Tao, A Vedaldi, ... Proceedings of the European conference on computer vision (ECCV), 670-685, 2018	113	2018
Dense semantic correspondence where every pixel is a classifier H Bristow, J Valmadre, S Lucey Proceedings of the IEEE International Conference on Computer Vision, 4024-4031, 2015	60	2015
General trajectory prior for non-rigid reconstruction J Valmadre, S Lucey Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on …, 2012	58	2012
Deterministic 3D human pose estimation using rigid structure J Valmadre, S Lucey Computer Vision–ECCV 2010, 467-480, 2010	53	2010
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, 204-219, 2014	34	2014
Devon: Deformable volume network for learning optical flow Y Lu, J Valmadre, H Wang, J Kannala, M Harandi, P Torr Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020	24	2020
Efficient articulated trajectory reconstruction using dynamic programming and filters J Valmadre, Y Zhu, S Sridharan, S Lucey	19	2012
Kronecker-markov prior for dynamic 3d reconstruction T Simon, J Valmadre, I Matthews, Y Sheikh IEEE transactions on pattern analysis and machine intelligence 39 (11), 2201 …, 2016	15	2016
Learning detectors quickly with stationary statistics J Valmadre, S Sridharan, S Lucey Asian Conference on Computer Vision, 99-114, 2014	12*	2014
The importance of estimating object extent when tracking with correlation filters L Bertinetto, J Valmadre, S Golodetz, O Miksik, PHS Torr Report for the Visual Object Tracking Workshop, 2015	8	2015
Computer Vision–ECCV 2016 Workshops. ECCV 2016 L Bertinetto, J Valmadre, JF Henriques, A Vedaldi, PHS Torr Lecture Notes in Computer Science 9914, 2016	6	2016
Closed-form solutions for low-rank non-rigid reconstruction J Valmadre, S Sridharan, S Denman, C Fookes, S Lucey 2015 International Conference on Digital Image Computing: Techniques and …, 2015	6	2015
Camera-less articulated trajectory reconstruction Y Zhu, J Valmadre, S Lucey	6*
Local metrics for multi-object tracking J Valmadre, A Bewley, J Huang, C Sun, C Sminchisescu, C Schmid arXiv preprint arXiv:2104.02631, 2021	5	2021
The VOT2015 challenge results M Kristan, J Matas, A Leonardis, M Felsberg, L Cehovin, G Fernandez, ... ICCV Workshop, 2015	2	2015
Learning with Neighbor Consistency for Noisy Labels A Iscen, J Valmadre, A Arnab, C Schmid Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022		2022

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