| Automatic Road Crack Detection Using Random Structured Forests zhiquan qi | 1323* | |
| Robust twin support vector machine for pattern classification Z Qi, Y Tian, Y Shi Pattern recognition 46 (1), 305-316, 2013 | 361 | 2013 |
| Nonparallel support vector machines for pattern classification Y Tian, Z Qi, X Ju, Y Shi, X Liu IEEE transactions on cybernetics 44 (7), 1067-1079, 2013 | 270 | 2013 |
| Laplacian twin support vector machine for semi-supervised classification Z Qi, Y Tian, Y Shi Neural networks 35, 46-53, 2012 | 223 | 2012 |
| Structural twin support vector machine for classification Z Qi, Y Tian, Y Shi Knowledge-based systems 43, 74-81, 2013 | 162 | 2013 |
| Twin support vector machine with universum data Z Qi, Y Tian, Y Shi Neural Networks 36, 112-119, 2012 | 150 | 2012 |
| Learning to incorporate structure knowledge for image inpainting J Yang, Z Qi, Y Shi Proceedings of the AAAI conference on artificial intelligence 34 (07), 12605 …, 2020 | 146 | 2020 |
| Unsupervised anomaly segmentation via deep feature reconstruction Y Shi, J Yang, Z Qi Neurocomputing 424, 9-22, 2021 | 142 | 2021 |
| Support vector machine classifier with truncated pinball loss X Shen, L Niu, Z Qi, Y Tian Pattern Recognition 68, 199-210, 2017 | 126 | 2017 |
| When ensemble learning meets deep learning: a new deep support vector machine for classification Z Qi, B Wang, Y Tian, P Zhang Knowledge-Based Systems 107, 54-60, 2016 | 87 | 2016 |
| Pavement distress detection using random decision forests L Cui, Z Qi, Z Chen, F Meng, Y Shi Data Science: Second International Conference, ICDS 2015, Sydney, Australia …, 2015 | 84 | 2015 |
| Dfr: Deep feature reconstruction for unsupervised anomaly segmentation J Yang, Y Shi, Z Qi arXiv preprint arXiv:2012.07122, 2020 | 77 | 2020 |
| Online multiple instance boosting for object detection Z Qi, Y Xu, L Wang, Y Song Neurocomputing 74 (10), 1769-1775, 2011 | 76 | 2011 |
| Improved twin support vector machine Y Tian, X Ju, Z Qi, Y Shi Science China Mathematics 57, 417-432, 2014 | 71 | 2014 |
| Efficient railway tracks detection and turnouts recognition method using HOG features Z Qi, Y Tian, Y Shi Neural Computing and Applications 23, 245-254, 2013 | 70 | 2013 |
| Support vector regression for newspaper/magazine sales forecasting X Yu, Z Qi, Y Zhao Procedia Computer Science 17, 1055-1062, 2013 | 67 | 2013 |
| A survey on semantic segmentation B Li, Y Shi, Z Qi, Z Chen 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 1233-1240, 2018 | 64 | 2018 |
| A novel clustering-based image segmentation via density peaks algorithm with mid-level feature Y Shi, Z Chen, Z Qi, F Meng, L Cui Neural Computing and Applications 28, 29-39, 2017 | 64 | 2017 |
| Image segmentation via improving clustering algorithms with density and distance Z Chen, Z Qi, F Meng, L Cui, Y Shi Procedia Computer Science 55, 1015-1022, 2015 | 52 | 2015 |
| Visual anomaly detection for images: A systematic survey J Yang, R Xu, Z Qi, Y Shi Procedia computer science 199, 471-478, 2022 | 51 | 2022 |
Yong ShiProfessor, University of Chinese Academy of SciencesEmail yang diverifikasi di ucas.ac.cn