Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation C Li, Q Zhong, D Xie, S Pu IJCAI 2018, 2018 | 572 | 2018 |
Skeleton-based action recognition with convolutional neural networks C Li, Q Zhong, D Xie, S Pu ICMEW 2017, 597-600, 2017 | 484* | 2017 |
Detection of co-salient objects by looking deep and wide D Zhang, J Han, C Li, J Wang, X Li IJCV 120 (2), 215-232, 2016 | 333 | 2016 |
Dynamic gcn: Context-enriched topology learning for skeleton-based action recognition F Ye, S Pu, Q Zhong, C Li, D Xie, H Tang Proceedings of the 28th ACM International Conference on Multimedia, 55-63, 2020 | 228 | 2020 |
A self-paced multiple-instance learning framework for co-saliency detection D Zhang, D Meng, C Li, L Jiang, Q Zhao, J Han ICCV 2015, 594-602, 2015 | 139 | 2015 |
Co-saliency detection via looking deep and wide D Zhang, J Han, C Li, J Wang CVPR 2015, 2994-3002, 2015 | 125 | 2015 |
Collaborative Spatiotemporal Feature Learning for Video Action Recognition C Li, Q Zhong, D Xie, S Pu CVPR 2019, 7872-7881, 2019 | 121 | 2019 |
Cascade region proposal and global context for deep object detection Q Zhong, C Li, Y Zhang, D Xie, S Yang, S Pu Neurocomputing 395, 170-177, 2020 | 61 | 2020 |
Towards good practices for recognition & detection Q Zhong, C Li, Y Zhang, H Sun, S Yang, D Xie, S Pu CVPR workshops 1, 3, 2016 | 23 | 2016 |
Language supervised training for skeleton-based action recognition W Xiang, C Li, Y Zhou, B Wang, L Zhang arXiv preprint arXiv:2208.05318, 2022 | 22 | 2022 |
Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition W Xiang, C Li, B Wang, X Wei, XS Hua, L Zhang ECCV 2022, 2022 | 18 | 2022 |
SP-ViT: Learning 2D Spatial Priors for Vision Transformers Y Zhou, W Xiang, C Li, B Wang, X Wei, L Zhang, M Keuper, X Hua BMVC 2022, 2022 | 6 | 2022 |
Team DEEP-HRI Moments in Time challenge 2018 technical report C Li, Z Hou, J Chen, Y Bu, J Zhou, Q Zhong, D Xie, S Pu Computer Vision and Pattern Recognition, 2018 | 5 | 2018 |