| 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 | 1128 | 2016 |
| Action recognition with trajectory-pooled deep-convolutional descriptors L Wang, Y Qiao, X Tang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4305-4314, 2015 | 819 | 2015 |
| Bag of visual words and fusion methods for action recognition: comprehensive study and good practice X Peng, L Wang, X Wang, Y Qiao Computer Vision and Image Understanding (CVIU) 150, 109－125, 2016 | 517 | 2016 |
| Towards good practices for very deep two-stream convnets L Wang, Y Xiong, Z Wang, Y Qiao arXiv preprint arXiv:1507.02159, 2015 | 260 | 2015 |
| Temporal action detection with structured segment networks Y Zhao, Y Xiong, L Wang, Z Wu, X Tang, D Lin International Conference on Computer Vision (ICCV), 2914-2923, 2017 | 224 | 2017 |
| Real-time action recognition with enhanced motion vector CNNs B Zhang, L Wang, Z Wang, Y Qiao, H Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2718-2726, 2016 | 215 | 2016 |
| Motionlets: mid-level 3D parts for human motion recognition L Wang, Y Qiao, X Tang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2674-2681, 2013 | 186 | 2013 |
| Multi-view super vector for action recognition Z Cai, L Wang, X Peng, Y Qiao IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 596 - 603, 2014 | 153 | 2014 |
| UntrimmedNets for weakly supervised action recognition and detection L Wang, Y Xiong, D Lin, L Van Gool IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4325－4334, 2017 | 137 | 2017 |
| A comparative study of encoding, pooling and normalization methods for action recognition X Wang, L Wang, Y Qiao Asian Conference on Computer Vision (ACCV), 572-585, 2012 | 132 | 2012 |
| Action recognition and detection by combining motion and appearance features L Wang, Y Qiao, X Tang THUMOS14 Action Recognition Challenge, 1-6, 2014 | 112 | 2014 |
| Video action detection with relational dynamic-poselets L Wang, Y Qiao, X Tang European Conference on Computer Vision (ECCV), 565-580, 2014 | 104 | 2014 |
| MoFAP: A multi-level representation for action recognition L Wang, Y Qiao, X Tang International Journal of Computer Vision 119 (3), 254-271, 2016 | 94 | 2016 |
| Latent hierarchical model of temporal structure for complex activity classification L Wang, Y Qiao, X Tang IEEE Transactions on Image Processing 23 (2), 810-822, 2014 | 92 | 2014 |
| Appearance-and-Relation Networks for Video Classification L Wang, W Li, W Li, L Van Gool IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1430-1439, 2018 | 84 | 2018 |
| Mining motion atoms and phrases for complex action recognition L Wang, Y Qiao, X Tang Proceedings of the IEEE international conference on computer vision, 2680-2687, 2013 | 81 | 2013 |
| Boosting VLAD with supervised dictionary learning and high-order statistics X Peng, L Wang, Y Qiao, Q Peng European Conference on Computer Vision (ECCV), 660-674, 2014 | 68 | 2014 |
| Places205-VGGNet models for scene recognition L Wang, S Guo, W Huang, Y Qiao arXiv preprint arXiv:1508.01667, 2015 | 67 | 2015 |
| 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 …, 2019 | 64 | 2019 |
| Knowledge guided disambiguation for large-scale scene classification with multi-resolution CNNs L Wang, S Guo, W Huang, Y Xiong, Y Qiao IEEE Transactions on Image Processing 26 (4), 2055-2068, 2017 | 62 | 2017 |
Yu QiaoProfessor of Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesVerified email at siat.ac.cn
