| A survey of appearance models in visual object tracking X Li, W Hu, C Shen, Z Zhang, A Dick, AVD Hengel ACM transactions on Intelligent Systems and Technology (TIST) 4 (4), 1-48, 2013 | 907 | 2013 |
| Deeply-Learned Part-Aligned Representations for Person Re-Identification L Zhao, X Li, J Wang, Y Zhuang International Conference on Computer Vision, 2017 | 857 | 2017 |
| Deepsaliency: Multi-task deep neural network model for salient object detection X Li, L Zhao, L Wei, MH Yang, F Wu, Y Zhuang, H Ling, J Wang IEEE transactions on image processing 25 (8), 3919-3930, 2016 | 579 | 2016 |
| FcaNet: Frequency Channel Attention Networks Z Qin, P Zhang, F Wu, X Li IEEE International Conference on Computer Vision, 2021 | 450 | 2021 |
| Ultra fast structure-aware deep lane detection Z Qin, H Wang, X Li Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 295 | 2020 |
| Contextual hypergraph modeling for salient object detection X Li, Y Li, C Shen, A Dick, A Van Den Hengel Proceedings of the IEEE international conference on computer vision, 3328-3335, 2013 | 240 | 2013 |
| Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality Z Qi, T Wang, G Song, W Hu, X Li IEEE Transactions on Knowledge and Data Engineering, 2017 | 226 | 2017 |
| Sequential particle swarm optimization for visual tracking X Zhang, W Hu, S Maybank, X Li, M Zhu 2008 IEEE conference on computer vision and pattern recognition, 1-8, 2008 | 214 | 2008 |
| Learning A Key-Value Memory Co-Attention Matching Network for Person ReIdentification Y Zhang, X Li, Z Zhang AAAI Conference on Artificial Intelligence (AAAI), 2019 | 213* | 2019 |
| Spatio-Temporal Graph Routing for Skeleton-based Action Recognition B Li, X Li, Z Zhang, F Wu AAAI Conference on Artificial Intelligence (AAAI), 2019 | 210 | 2019 |
| A Survey on Generative Adversarial Networks: Variants, Applications, and Training A Jabbar, X Li, B Omar ACM Computing Surveys, 2021 | 196 | 2021 |
| Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model W Hu, X Li, W Luo, X Zhang, S Maybank, Z Zhang Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1-1, 2012 | 186 | 2012 |
| Robust visual tracking based on incremental tensor subspace learning X Li, W Hu, Z Zhang, X Zhang, G Luo 2007 IEEE 11th international conference on computer vision, 1-8, 2007 | 173 | 2007 |
| Incremental tensor subspace learning and its applications to foreground segmentation and tracking W Hu, X Li, X Zhang, X Shi, S Maybank, Z Zhang International Journal of Computer Vision, 1-25, 2011 | 171 | 2011 |
| Visual tracking via incremental log-Euclidean Riemannian subspace learning X Li, W Hu, Z Zhang, X Zhang, M Zhu, J Cheng Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on …, 2008 | 169 | 2008 |
| Learning hash functions using column generation X Li, G Lin, C Shen, A Hengel, A Dick International Conference on Machine Learning, 2013 | 158 | 2013 |
| Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion P Sun, W Zhang, H Wang, S Li, X Li CVPR 2021 Oral, 2021 | 136 | 2021 |
| Body structure aware deep crowd counting S Huang, X Li, Z Zhang, F Wu, S Gao, R Ji, J Han IEEE Transactions on Image Processing 27 (3), 1049-1059, 2017 | 130 | 2017 |
| A coarse-to-fine strategy for vehicle motion trajectory clustering X Li, W Hu, W Hu 18th International conference on pattern recognition (ICPR'06) 1, 591-594, 2006 | 130 | 2006 |
| An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval W Hu, X Li, G Tian, S Maybank, Z Zhang IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 35 (5), 1051-1065, 2013 | 128 | 2013 |
Zhongfei (Mark) ZhangProfessor of comuter science, Binghamton UniversityVerified email at binghamton.edu
