| Controllable text generation Z Hu, Z Yang, X Liang, R Salakhutdinov, EP Xing arXiv preprint arXiv:1703.00955 4, 2017 | 1064* | 2017 |
| Is faster R-CNN doing well for pedestrian detection? L Zhang, L Lin, X Liang, K He Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 925 | 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 | 776 | 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 | 732 | 2017 |
| 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 | 673 | 2017 |
| 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 | 645* | 2018 |
| 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 | 538 | 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, ... USENIX Annual Technical Conference 1 (1), 1.2, 2017 | 398* | 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 | 365 | 2017 |
| Semantic object parsing with graph lstm X Liang, X Shen, J Feng, L Lin, S Yan Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 363 | 2016 |
| Meta r-cnn: Towards general solver for instance-level low-shot learning X Yan, Z Chen, A Xu, X Wang, X Liang, L Lin Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 301 | 2019 |
| 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 | 301 | 2015 |
| Peak-piloted deep network for facial expression recognition X Zhao, X Liang, L Liu, T Li, Y Han, N Vasconcelos, S Yan Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 294 | 2016 |
| Rethinking knowledge graph propagation for zero-shot learning M Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang, EP Xing Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 277 | 2019 |
| 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 | 275 | 2017 |
| Instance-level human parsing via part grouping network K Gong, X Liang, Y Li, Y Chen, M Yang, L Lin Proceedings of the European conference on computer vision (ECCV), 770-785, 2018 | 272 | 2018 |
| 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 | 267 | 2015 |
| Toward characteristic-preserving image-based virtual try-on network B Wang, H Zheng, X Liang, Y Chen, L Lin, M Yang Proceedings of the European conference on computer vision (ECCV), 589-604, 2018 | 256 | 2018 |
| 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 | 251 | 2017 |
| 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 | 223 | 2016 |
Liang LinFellow of IAPR, Professor of Computer Science, Sun Yat-Sen University, China在 ieee.org 的電子郵件地址已通過驗證
