| Temporal segment networks: Towards good practices for deep action recognition L Wang, Y Xiong, Z Wang, Y Qiao, D Lin, X Tang, L Van Gool European conference on computer vision, 20-36, 2016 | 3629 | 2016 |
| Spatial temporal graph convolutional networks for skeleton-based action recognition S Yan, Y Xiong, D Lin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2970 | 2018 |
| Unsupervised feature learning via non-parametric instance discrimination Z Wu, Y Xiong, SX Yu, D Lin Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 2667 | 2018 |
| MMDetection: Open mmlab detection toolbox and benchmark K Chen, J Wang, J Pang, Y Cao, Y Xiong, X Li, S Sun, W Feng, Z Liu, J Xu, ... arXiv preprint arXiv:1906.07155, 2019 | 1941 | 2019 |
| Libra r-cnn: Towards balanced learning for object detection J Pang, K Chen, J Shi, H Feng, W Ouyang, D Lin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1152 | 2019 |
| Hybrid task cascade for instance segmentation K Chen, J Pang, J Wang, Y Xiong, X Li, S Sun, W Feng, Z Liu, J Shi, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1027 | 2019 |
| Psanet: Point-wise spatial attention network for scene parsing H Zhao, Y Zhang, S Liu, J Shi, CC Loy, D Lin, J Jia Proceedings of the European conference on computer vision (ECCV), 267-283, 2018 | 898 | 2018 |
| Temporal action detection with structured segment networks Y Zhao, Y Xiong, L Wang, Z Wu, X Tang, D Lin Proceedings of the IEEE international conference on computer vision, 2914-2923, 2017 | 879 | 2017 |
| Learning a unified classifier incrementally via rebalancing S Hou, X Pan, CC Loy, Z Wang, D Lin Proceedings of the IEEE/CVF conference on Computer Vision and Pattern …, 2019 | 641 | 2019 |
| Temporal segment networks for action recognition in videos L Wang, Y Xiong, Z Wang, Y Qiao, D Lin, X Tang, L Van Gool IEEE transactions on pattern analysis and machine intelligence 41 (11), 2740 …, 2018 | 605 | 2018 |
| Towards diverse and natural image descriptions via a conditional gan B Dai, S Fidler, R Urtasun, D Lin Proceedings of the IEEE international conference on computer vision, 2970-2979, 2017 | 594 | 2017 |
| Region proposal by guided anchoring J Wang, K Chen, S Yang, CC Loy, D Lin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 560 | 2019 |
| Untrimmednets for weakly supervised action recognition and detection L Wang, Y Xiong, D Lin, L Van Gool Proceedings of the IEEE conference on Computer Vision and Pattern …, 2017 | 507 | 2017 |
| Detecting visual relationships with deep relational networks B Dai, Y Zhang, D Lin Proceedings of the IEEE conference on computer vision and Pattern …, 2017 | 504 | 2017 |
| Holistic scene understanding for 3d object detection with rgbd cameras D Lin, S Fidler, R Urtasun Proceedings of the IEEE international conference on computer vision, 1417-1424, 2013 | 343 | 2013 |
| Image tagging based upon cross domain context SJ Baker, A Kapoor, G Hua, D Lin US Patent 8,645,287, 2014 | 291 | 2014 |
| Adapting object detectors via selective cross-domain alignment X Zhu, J Pang, C Yang, J Shi, D Lin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 288 | 2019 |
| Polynet: A pursuit of structural diversity in very deep networks X Zhang, Z Li, C Change Loy, D Lin Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 279 | 2017 |
| Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes J Zhang, MP Landry, PW Barone, JH Kim, S Lin, ZW Ulissi, D Lin, B Mu, ... Nature nanotechnology 8 (12), 959-968, 2013 | 274 | 2013 |
| Conditional infomax learning: An integrated framework for feature extraction and fusion D Lin, X Tang Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz …, 2006 | 265 | 2006 |
Xiaoou TangThe Chinese University of Hong KongVerified email at ie.cuhk.edu.hk
