Professor Javen Qinfeng Shi

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Anton van den HengelDirector, Australian Institute for Machine Learning, UoA | Director of Applied Science, AmazonVerified email at adelaide.edu.au
Chunhua ShenVerified email at adelaide.edu.au
Ian D ReidUniversity of AdelaideVerified email at adelaide.edu.au
Zhen ZhangThe University of AdelaideVerified email at adelaide.edu.au
Hamid RezatofighiAssistant Professor, Monash University, Melbourne, AustraliaVerified email at monash.edu
Mingkui TanSouth China University of TechnologyVerified email at scut.edu.cn
Julian McAuleyProfessor, UC San DiegoVerified email at eng.ucsd.edu
Damith C. RanasingheThe University of Adelaide, University of CambridgeVerified email at cs.adelaide.edu.au
Alex SmolaAmazon Web ServicesVerified email at smola.org
Rui YaoPh.DVerified email at adelaide.edu.au
Anthony DickComputer Science, University of AdelaideVerified email at adelaide.edu.au
Professor Junbin GaoDiscipline of Business Analytics in Business School, The University of SydneyVerified email at sydney.edu.au
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
Anton Milan (Andriyenko)AmazonVerified email at amazon.com
Lingqiao LiuSenior Lecturer of the University of AdelaideVerified email at adelaide.edu.au
Guosheng LinNanyang Technological UniversityVerified email at ntu.edu.sg
Ehsan AbbasnejadAustralian Institute for Machine Learning, University of AdelaideVerified email at adelaide.edu.au
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Roberto L. Shinmoto TorresThe University of AdelaideVerified email at adelaide.edu.au
Li ChengAssociate Professor, Department of ECE, University of Alberta, Canada; Adjunct staff, BII, A*STARVerified email at ualberta.ca

Professor Javen Qinfeng Shi

Director for Probabilistic Graphical Model Group, UoA | DeepSightX | Smarter Regions

Verified email at adelaide.edu.au - Homepage

Probabilistic Graphical Models Counterfactuals Causality Machine Learning Computer Vision


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Image-based recommendations on styles and substitutes J McAuley, C Targett, Q Shi, A Van Den Hengel Proceedings of the 38th international ACM SIGIR conference on research and …, 2015	1327	2015
Real-time visual tracking using compressive sensing H Li, C Shen, Q Shi CVPR 2011, 1305-1312, 2011	414	2011
Fast supervised hashing with decision trees for high-dimensional data G Lin, C Shen, Q Shi, A Van den Hengel, D Suter Proceedings of the IEEE conference on computer vision and pattern …, 2014	411	2014
Is face recognition really a compressive sensing problem? Q Shi, A Eriksson, A Van Den Hengel, C Shen CVPR 2011, 553-560, 2011	340	2011
Joint probabilistic data association revisited SH Rezatofighi, A Milan, Z Zhang, Q Shi, A Dick, I Reid Proceedings of the IEEE international conference on computer vision, 3047-3055, 2015	300	2015
Inductive Hashing on Manifolds F Shen, C Shen, Q Shi, A Hengel, Z Tang IEEE Conference on Computer Vision and Pattern Recognition (26th: 2013 …, 2013	241	2013
Hash kernels for structured data. Q Shi, J Petterson, G Dror, J Langford, A Smola, SVN Vishwanathan Journal of Machine Learning Research 10 (11), 2009	235	2009
From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur D Gong, J Yang, L Liu, Y Zhang, I Reid, C Shen, A Van Den Hengel, Q Shi Proceedings of the IEEE conference on computer vision and pattern …, 2017	231	2017
Part-based visual tracking with online latent structural learning R Yao, Q Shi, C Shen, Y Zhang, A Van Den Hengel Proceedings of the IEEE conference on computer vision and pattern …, 2013	223	2013
Hashing on nonlinear manifolds F Shen, C Shen, Q Shi, A Van den Hengel, Z Tang, HT Shen IEEE Transactions on Image Processing 24 (6), 1839-1851, 2015	152	2015
Discriminative human action segmentation and recognition using semi-Markov model Q Shi, L Wang, L Cheng, A Smola 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008	152	2008
Human action segmentation and recognition using discriminative semi-markov models Q Shi, L Cheng, L Wang, A Smola International journal of computer vision 93 (1), 22-32, 2011	125	2011
Hash kernels Q Shi, J Petterson, G Dror, J Langford, A Smola, A Strehl, ... Artificial intelligence and statistics, 496-503, 2009	124	2009
Dual graph regularized latent low-rank representation for subspace clustering M Yin, J Gao, Z Lin, Q Shi, Y Guo IEEE Transactions on Image Processing 24 (12), 4918-4933, 2015	110	2015
Sensor enabled wearable RFID technology for mitigating the risk of falls near beds RLS Torres, DC Ranasinghe, Q Shi, AP Sample 2013 IEEE international conference on RFID (RFID), 191-198, 2013	94	2013
Seeing deeply and bidirectionally: A deep learning approach for single image reflection removal J Yang, D Gong, L Liu, Q Shi Proceedings of the european conference on computer vision (ECCV), 654-669, 2018	83	2018
Non-sparse linear representations for visual tracking with online reservoir metric learning X Li, C Shen, Q Shi, A Dick, A Van Den Hengel 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1760-1767, 2012	82	2012
Attention-guided network for ghost-free high dynamic range imaging Q Yan, D Gong, Q Shi, A Hengel, C Shen, I Reid, Y Zhang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	66	2019
Cluster sparsity field: An internal hyperspectral imagery prior for reconstruction L Zhang, W Wei, Y Zhang, C Shen, A Van Den Hengel, Q Shi International Journal of Computer Vision 126 (8), 797-821, 2018	65	2018
Blind image deconvolution by automatic gradient activation D Gong, M Tan, Y Zhang, A Van den Hengel, Q Shi Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	63	2016

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