| Discrimination-aware channel pruning for deep neural networks Z Zhuang, M Tan, B Zhuang, J Liu, Y Guo, Q Wu, J Huang, J Zhu Advances in neural information processing systems 31, 2018 | 609 | 2018 |
| Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation C Zhang, G Lin, F Liu, J Guo, Q Wu, R Yao Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 227 | 2019 |
| Stratified sampling for feature subspace selection in random forests for high dimensional data Y Ye, Q Wu, JZ Huang, MK Ng, X Li Pattern Recognition 46 (3), 769-787, 2013 | 181 | 2013 |
| Visual grounding via accumulated attention C Deng, Q Wu, Q Wu, F Hu, F Lyu, M Tan Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 167 | 2018 |
| A unified framework for metric transfer learning Y Xu, SJ Pan, H Xiong, Q Wu, R Luo, H Min, H Song IEEE Transactions on Knowledge and Data Engineering 29 (6), 1158-1171, 2017 | 161 | 2017 |
| Attention guided network for retinal image segmentation S Zhang, H Fu, Y Yan, Y Zhang, Q Wu, M Yang, M Tan, Y Xu Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 153 | 2019 |
| ForesTexter: an efficient random forest algorithm for imbalanced text categorization Q Wu, Y Ye, H Zhang, MK Ng, SS Ho Knowledge-Based Systems 67, 105-116, 2014 | 147 | 2014 |
| Online transfer learning with multiple homogeneous or heterogeneous sources Q Wu, H Wu, X Zhou, M Tan, Y Xu, Y Yan, T Hao IEEE Transactions on Knowledge and Data Engineering 29 (7), 1494-1507, 2017 | 108 | 2017 |
| Collaborative unsupervised domain adaptation for medical image diagnosis Y Zhang, Y Wei, Q Wu, P Zhao, S Niu, J Huang, M Tan IEEE Transactions on Image Processing 29, 7834-7844, 2020 | 102 | 2020 |
| ML-FOREST: A multi-label tree ensemble method for multi-label classification Q Wu, M Tan, H Song, J Chen, MK Ng IEEE transactions on knowledge and data engineering 28 (10), 2665-2680, 2016 | 98 | 2016 |
| Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests TT Nguyen, JZ Huang, Q Wu, TT Nguyen, MJ Li BMC genomics 16, 1-11, 2015 | 97 | 2015 |
| Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation. Y Yan, W Li, H Wu, H Min, M Tan, Q Wu IJCAI 7, 2969-2975, 2018 | 96 | 2018 |
| MR-NTD: Manifold regularization nonnegative tucker decomposition for tensor data dimension reduction and representation X Li, MK Ng, G Cong, Y Ye, Q Wu IEEE transactions on neural networks and learning systems 28 (8), 1787-1800, 2016 | 94 | 2016 |
| Breaking winner-takes-all: Iterative-winners-out networks for weakly supervised temporal action localization R Zeng, C Gan, P Chen, W Huang, Q Wu, M Tan IEEE Transactions on Image Processing 28 (12), 5797-5808, 2019 | 75 | 2019 |
| Auto-embedding generative adversarial networks for high resolution image synthesis Y Guo, Q Chen, J Chen, Q Wu, Q Shi, M Tan IEEE Transactions on Multimedia 21 (11), 2726-2737, 2019 | 73 | 2019 |
| Deep guidance network for biomedical image segmentation P Yin, R Yuan, Y Cheng, Q Wu IEEE access 8, 116106-116116, 2020 | 67 | 2020 |
| Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation. Y Yan, W Li, MKP Ng, M Tan, H Wu, H Min, Q Wu IJCAI, 3252-3258, 2017 | 61 | 2017 |
| Supervised feature selection with a stratified feature weighting method R Chen, N Sun, X Chen, M Yang, Q Wu IEEE Access 6, 15087-15098, 2018 | 59 | 2018 |
| SNP selection and classification of genome-wide SNP data using stratified sampling random forests Q Wu, Y Ye, Y Liu, MK Ng IEEE transactions on nanobioscience 11 (3), 216-227, 2012 | 58 | 2012 |
| COVID-DA: Deep domain adaptation from typical pneumonia to COVID-19 Y Zhang, S Niu, Z Qiu, Y Wei, P Zhao, J Yao, J Huang, Q Wu, M Tan arXiv preprint arXiv:2005.01577, 2020 | 57 | 2020 |
Mingkui TanSouth China University of TechnologyBestätigte E-Mail-Adresse bei scut.edu.cn
