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Mehrtash Harandi
Mehrtash Harandi
Department of Electrical and Computer Systems Engineering, Monash University
Verified email at monash.edu - Homepage
Title
Cited by
Cited by
Year
Going deeper into action recognition: A survey
S Herath, M Harandi, F Porikli
Image and vision computing 60, 4-21, 2017
6272017
Unsupervised domain adaptation by domain invariant projection
M Baktashmotlagh, MT Harandi, BC Lovell, M Salzmann
Proceedings of the IEEE international conference on computer vision, 769-776, 2013
4682013
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching
MT Harandi, C Sanderson, S Shirazi, BC Lovell
CVPR 2011, 2705-2712, 2011
3392011
Kernel methods on the Riemannian manifold of symmetric positive definite matrices
S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi
proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013
3252013
Adaptive subspaces for few-shot learning
C Simon, P Koniusz, R Nock, M Harandi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
2802020
Sparse coding and dictionary learning for symmetric positive definite matrices: A kernel approach
MT Harandi, C Sanderson, R Hartley, BC Lovell
Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012
2342012
From manifold to manifold: Geometry-aware dimensionality reduction for SPD matrices
MT Harandi, M Salzmann, R Hartley
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014
2122014
Spatio-temporal covariance descriptors for action and gesture recognition
A Sanin, C Sanderson, MT Harandi, BC Lovell
2013 IEEE Workshop on applications of Computer Vision (WACV), 103-110, 2013
2102013
Kernel methods on Riemannian manifolds with Gaussian RBF kernels
S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi
IEEE transactions on pattern analysis and machine intelligence 37 (12), 2464 …, 2015
2082015
Dimensionality reduction on SPD manifolds: The emergence of geometry-aware methods
M Harandi, M Salzmann, R Hartley
IEEE transactions on pattern analysis and machine intelligence 40 (1), 48-62, 2017
1752017
Hierarchical neural architecture search for deep stereo matching
X Cheng, Y Zhong, M Harandi, Y Dai, X Chang, H Li, T Drummond, Z Ge
Advances in Neural Information Processing Systems 33, 22158-22169, 2020
1662020
Dictionary learning and sparse coding on Grassmann manifolds: An extrinsic solution
M Harandi, C Sanderson, C Shen, BC Lovell
Proceedings of the IEEE international conference on computer vision, 3120-3127, 2013
1582013
Deep unsupervised saliency detection: A multiple noisy labeling perspective
J Zhang, T Zhang, Y Dai, M Harandi, R Hartley
Proceedings of the IEEE conference on computer vision and pattern …, 2018
1562018
Domain adaptation on the statistical manifold
M Baktashmotlagh, MT Harandi, BC Lovell, M Salzmann
Proceedings of the IEEE conference on computer vision and pattern …, 2014
1252014
Bregman divergences for infinite dimensional covariance matrices
M Harandi, M Salzmann, F Porikli
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014
1212014
Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures
MT Harandi, C Sanderson, A Wiliem, BC Lovell
2012 IEEE Workshop on the Applications of Computer Vision (WACV), 433-439, 2012
1202012
Learning an invariant hilbert space for domain adaptation
S Herath, M Harandi, F Porikli
Proceedings of the IEEE conference on computer vision and pattern …, 2017
1142017
Bilinear attention networks for person retrieval
P Fang, J Zhou, SK Roy, L Petersson, M Harandi
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
1112019
Riemannian coding and dictionary learning: Kernels to the rescue
M Harandi, M Salzmann
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
1052015
Expanding the family of grassmannian kernels: An embedding perspective
MT Harandi, M Salzmann, S Jayasumana, R Hartley, H Li
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014
992014
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