| Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression Recognition M Liu, S Shan, R Wang, X Chen Computer Vision and Pattern Recognition. CVPR 2014. IEEE, 1749-1756, 2014 | 407 | 2014 |
| Deeply Learning Deformable Facial Action Parts Model for Dynamic Expression Analysis M Liu, S Li, S Shan, R Wang, X Chen Asian Conference on Computer Vision. ACCV 2014. Springer, 143-157, 2014 | 351 | 2014 |
| AU-aware Deep Networks for Facial Expression Recognition M Liu, S Li, S Shan, X Chen Automatic Face and Gesture Recognition. FG 2013. IEEE, 1-6, 2013 | 297 | 2013 |
| Au-inspired deep networks for facial expression feature learning M Liu, S Li, S Shan, X Chen Neurocomputing 159, 126-136, 2015 | 240 | 2015 |
| Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild M Liu, R Wang, S Li, S Shan, Z Huang, X Chen International Conference on Multimodal Interaction. ICMI 2014. ACM, 494-501, 2014 | 205 | 2014 |
| Partial least squares regression on grassmannian manifold for emotion recognition M Liu, R Wang, Z Huang, S Shan, X Chen International Conference on Multimodal Interaction. ICMI 2013. ACM, 525-530, 2013 | 74 | 2013 |
| A comprehensive survey on automatic facial action unit analysis R Zhi, M Liu, D Zhang The Visual Computer 36, 1067-1093, 2020 | 73 | 2020 |
| Learning expressionlets via universal manifold model for dynamic facial expression recognition M Liu, S Shan, R Wang, X Chen IEEE Transactions on Image Processing 25 (12), 5920-5932, 2016 | 48 | 2016 |
| Enhancing expression recognition in the wild with unlabeled reference data M Liu, S Li, S Shan, X Chen Asian Conference on Computer Vision. ACCV 2012. Springer, 577-588, 2012 | 29 | 2012 |
| Distortion-aware monocular depth estimation for omnidirectional images HX Chen, K Li, Z Fu, M Liu, Z Chen, Y Guo IEEE Signal Processing Letters 28, 334-338, 2021 | 20 | 2021 |
| Exploiting Feature Hierarchies With Convolutional Neural Networks for Cultural Event Recognition M Liu, X Liu, Y Li, X Chen, A Hauptmann, S Shan IEEE International Conference on Computer Vision (ICCV), 2015 | 17 | 2015 |
| 深度学习: 多层神经网络的复兴与变革 山世光， 阚美娜， 刘昕， 刘梦怡， 邬书哲 科技导报 34 (14), 60-70, 2016 | 13 | 2016 |
| Content-based video relevance prediction challenge: Data, protocol, and baseline (ACM Multimedia 2018 CBVRP Grand Challenge) M Liu, X Xie, H Zhou arXiv preprint arXiv:1806.00737, 2018 | 12 | 2018 |
| Pano-SfMLearner: Self-supervised Multi-task Learning of Depth and Semantics in Panoramic Videos M Liu, S Wang, Y Guo, Y He, H Xue IEEE Signal Processing Letters 28, 832-836, 2021 | 10 | 2021 |
| Video modeling and learning on Riemannian manifold for emotion recognition in the wild M Liu, R Wang, S Li, Z Huang, S Shan, X Chen Journal on Multimodal User Interfaces 10, 113-124, 2016 | 7 | 2016 |
| Deep learning: the revival and transformation of multilayer neural networks SG Shan, MN Kan, X Liu, MY Liu, SZ Wu Science & Technology Review 34 (14), 60-70, 2016 | 7 | 2016 |
| Heterogeneous Face Recognition and Synthesis Using Canonical Correlation Analysis (CCA) M Liu, Z Yuan, Y Ma, X Chen, Q Yin Journal of Convergence Information Technology 7 (8), 398-407, 2012 | 7 | 2012 |
| Heterogeneous face biometrics based on Guassian weights and invariant features synthesis M Liu, W Xie, X Chen, Y Ma, Y Guo, J Meng, Z Yuan, Q Qin Computing, Control and Industrial Engineering. CCIE 2011. IEEE 2, 374-377, 2011 | 7 | 2011 |
| Skipping word: A character-sequential representation based framework for question answering L Meng, Y Li, M Liu, P Shu Proceedings of the 25th acm international on conference on information and …, 2016 | 6 | 2016 |
| Learning mid-level words on Riemannian manifold for action recognition M Liu, R Wang, S Shan, X Chen arXiv preprint arXiv:1511.04808, 2015 | 6 | 2015 |
Shiguang ShanProfessor of Institute of Computing Technology, Chinese Academy of SciencesПотвърден имейл адрес: ict.ac.cn
