| Cost-sensitive learning of deep feature representations from imbalanced data SH Khan, M Hayat, M Bennamoun, FA Sohel, R Togneri IEEE transactions on neural networks and learning systems 29 (8), 3573-3587, 2017 | 378 | 2017 |
| Deep reconstruction models for image set classification M Hayat, M Bennamoun, S An IEEE transactions on pattern analysis and machine intelligence 37 (4), 713-727, 2014 | 160 | 2014 |
| An efficient 3D face recognition approach using local geometrical signatures Y Lei, M Bennamoun, M Hayat, Y Guo Pattern Recognition 47 (2), 509-524, 2014 | 116 | 2014 |
| Finding task-relevant features for few-shot learning by category traversal H Li, D Eigen, S Dodge, M Zeiler, X Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 86 | 2019 |
| Learning Non-Linear Reconstruction Models for Image Set Classification M Hayat, M Bennamoun, S An Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 84 | 2014 |
| A two-phase weighted collaborative representation for 3D partial face recognition with single sample Y Lei, Y Guo, M Hayat, M Bennamoun, X Zhou Pattern Recognition 52, 218-237, 2016 | 63 | 2016 |
| A discriminative representation of convolutional features for indoor scene recognition SH Khan, M Hayat, M Bennamoun, R Togneri, FA Sohel IEEE Transactions on Image Processing 25 (7), 3372-3383, 2016 | 62 | 2016 |
| A spatial layout and scale invariant feature representation for indoor scene classification M Hayat, SH Khan, M Bennamoun, S An IEEE Transactions on Image Processing 25 (10), 4829-4841, 2016 | 48 | 2016 |
| Striking the right balance with uncertainty S Khan, M Hayat, SW Zamir, J Shen, L Shao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 44 | 2019 |
| An automatic framework for textured 3D video-based facial expression recognition M Hayat, M Bennamoun IEEE Transactions on Affective Computing 5 (3), 301-313, 2014 | 44 | 2014 |
| Regularization of deep neural networks with spectral dropout SH Khan, M Hayat, F Porikli Neural Networks 110, 82-90, 2019 | 42 | 2019 |
| Adversarial defense by restricting the hidden space of deep neural networks A Mustafa, S Khan, M Hayat, R Goecke, J Shen, L Shao Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 40 | 2019 |
| Image super-resolution as a defense against adversarial attacks A Mustafa, SH Khan, M Hayat, J Shen, L Shao IEEE Transactions on Image Processing 29, 1711-1724, 2019 | 35 | 2019 |
| An RGB–D based image set classification for robust face recognition from Kinect data M Hayat, M Bennamoun, AA El-Sallam Neurocomputing 171, 889-900, 2016 | 34 | 2016 |
| Reverse training: An efficient approach for image set classification M Hayat, M Bennamoun, S An European conference on computer vision, 784-799, 2014 | 30 | 2014 |
| Investigating word affect features and fusion of probabilistic predictions incorporating uncertainty in AVEC 2017 T Dang, B Stasak, Z Huang, S Jayawardena, M Atcheson, M Hayat, P Le, ... Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, 27-35, 2017 | 29 | 2017 |
| Joint Registration and Representation Learning for Unconstrained Face Identification M Hayat, SH Khan, N Werghi, R Goecke The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 | 29 | 2017 |
| Empowering simple binary classifiers for image set based face recognition M Hayat, SH Khan, M Bennamoun International Journal of Computer Vision 123 (3), 479-498, 2017 | 25 | 2017 |
| Scene categorization with spectral features SH Khan, M Hayat, F Porikli Proceedings of the IEEE international conference on computer vision, 5638-5648, 2017 | 21 | 2017 |
| Gaussian affinity for max-margin class imbalanced learning M Hayat, S Khan, SW Zamir, J Shen, L Shao Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 19 | 2019 |
Salman KhanMBZUAI; Australian National UniversityVerified email at anu.edu.au
