| Controllable text generation Z Hu, Z Yang, X Liang, R Salakhutdinov, EP Xing arXiv preprint arXiv:1703.00955 4, 2017 | 742* | 2017 |
| Is faster R-CNN doing well for pedestrian detection? L Zhang, L Lin, X Liang, K He European conference on computer vision, 443-457, 2016 | 696 | 2016 |
| Scale-aware fast R-CNN for pedestrian detection J Li, X Liang, SM Shen, T Xu, J Feng, S Yan IEEE transactions on Multimedia 20 (4), 985-996, 2017 | 549 | 2017 |
| Object region mining with adversarial erasing: A simple classification to semantic segmentation approach Y Wei, J Feng, X Liang, MM Cheng, Y Zhao, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 426 | 2017 |
| Stc: A simple to complex framework for weakly-supervised semantic segmentation Y Wei, X Liang, Y Chen, X Shen, MM Cheng, J Feng, Y Zhao, S Yan IEEE transactions on pattern analysis and machine intelligence 39 (11), 2314 …, 2016 | 397 | 2016 |
| Perceptual generative adversarial networks for small object detection J Li, X Liang, Y Wei, T Xu, J Feng, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 387 | 2017 |
| Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing K Gong, X Liang, D Zhang, X Shen, L Lin Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 293 | 2017 |
| Semantic object parsing with graph lstm X Liang, X Shen, J Feng, L Lin, S Yan European Conference on Computer Vision, 125-143, 2016 | 267 | 2016 |
| Poseidon: An efficient communication architecture for distributed deep learning on {GPU} clusters H Zhang, Z Zheng, S Xu, W Dai, Q Ho, X Liang, Z Hu, J Wei, P Xie, ... 2017 {USENIX} Annual Technical Conference ({USENIX}{ATC} 17), 181-193, 2017 | 259* | 2017 |
| Dual motion GAN for future-flow embedded video prediction X Liang, L Lee, W Dai, EP Xing proceedings of the IEEE international conference on computer vision, 1744-1752, 2017 | 250 | 2017 |
| Human parsing with contextualized convolutional neural network X Liang, C Xu, X Shen, J Yang, S Liu, J Tang, L Lin, S Yan Proceedings of the IEEE international conference on computer vision, 1386-1394, 2015 | 227 | 2015 |
| Proposal-free network for instance-level object segmentation X Liang, L Lin, Y Wei, X Shen, J Yang, S Yan IEEE transactions on pattern analysis and machine intelligence 40 (12), 2978 …, 2017 | 213 | 2017 |
| Deep variation-structured reinforcement learning for visual relationship and attribute detection X Liang, L Lee, EP Xing Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 208 | 2017 |
| Deep human parsing with active template regression X Liang, S Liu, X Shen, J Yang, L Liu, J Dong, L Lin, S Yan IEEE transactions on pattern analysis and machine intelligence 37 (12), 2402 …, 2015 | 205 | 2015 |
| Peak-piloted deep network for facial expression recognition X Zhao, X Liang, L Liu, T Li, Y Han, N Vasconcelos, S Yan European conference on computer vision, 425-442, 2016 | 190 | 2016 |
| Look into person: Joint body parsing & pose estimation network and a new benchmark X Liang, K Gong, X Shen, L Lin IEEE transactions on pattern analysis and machine intelligence 41 (4), 871-885, 2018 | 166 | 2018 |
| Recurrent topic-transition gan for visual paragraph generation X Liang, Z Hu, H Zhang, C Gan, EP Xing Proceedings of the IEEE international conference on computer vision, 3362-3371, 2017 | 164 | 2017 |
| Semantic object parsing with local-global long short-term memory X Liang, X Shen, D Xiang, J Feng, L Lin, S Yan Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 163 | 2016 |
| Attentive contexts for object detection J Li, Y Wei, X Liang, J Dong, T Xu, J Feng, S Yan IEEE Transactions on Multimedia 19 (5), 944-954, 2016 | 154 | 2016 |
| Matching-cnn meets knn: Quasi-parametric human parsing S Liu, X Liang, L Liu, X Shen, J Yang, C Xu, L Lin, X Cao, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 154 | 2015 |
Liang LinProfessor, School of Computer Science and Engineering, Sun Yat-Sen University, China在 ieee.org 的電子郵件地址已通過驗證
