| Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation C Li, Q Zhong, D Xie, S Pu arXiv preprint arXiv:1804.06055, 2018 | 563 | 2018 |
| Self-supervised spatiotemporal learning via video clip order prediction D Xu, J Xiao, Z Zhao, J Shao, D Xie, Y Zhuang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 487 | 2019 |
| Skeleton-based action recognition with convolutional neural networks C Li, Q Zhong, D Xie, S Pu Multimedia & Expo Workshops (ICMEW), 2017 IEEE International Conference on …, 2017 | 426 | 2017 |
| 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 | 218 | 2020 |
| All you need is beyond a good init: Exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation D Xie, J Xiong, S Pu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 196 | 2017 |
| Fusing geometric features for skeleton-based action recognition using multilayer LSTM networks S Zhang, Y Yang, J Xiao, X Liu, Y Yang, D Xie, Y Zhuang IEEE Transactions on Multimedia 20 (9), 2330-2343, 2018 | 190 | 2018 |
| Advancing image understanding in poor visibility environments: A collective benchmark study W Yang, Y Yuan, W Ren, J Liu, WJ Scheirer, Z Wang, T Zhang, Q Zhong, ... IEEE Transactions on Image Processing 29, 5737-5752, 2020 | 188 | 2020 |
| Small-scale pedestrian detection based on topological line localization and temporal feature aggregation T Song, L Sun, D Xie, H Sun, S Pu Proceedings of the European Conference on Computer Vision (ECCV), 536-551, 2018 | 154 | 2018 |
| Collaborative spatiotemporal feature learning for video action recognition C Li, Q Zhong, D Xie, S Pu Proceedings of the ieee/cvf conference on computer vision and pattern …, 2019 | 119 | 2019 |
| Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection J Hou, Y Zhang, Q Zhong, D Xie, S Pu, H Zhou Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 118 | 2021 |
| A free lunch for unsupervised domain adaptive object detection without source data X Li, W Chen, D Xie, S Yang, P Yuan, S Pu, Y Zhuang Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8474-8481, 2021 | 113 | 2021 |
| All you need is a few shifts: Designing efficient convolutional neural networks for image classification W Chen, D Xie, Y Zhang, S Pu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 101 | 2019 |
| Topology-aware convolutional neural network for efficient skeleton-based action recognition K Xu, F Ye, Q Zhong, D Xie Proceedings of the AAAI Conference on Artificial Intelligence 36 (3), 2866-2874, 2022 | 81 | 2022 |
| Extreme network compression via filter group approximation B Peng, W Tan, Z Li, S Zhang, D Xie, S Pu Proceedings of the European Conference on Computer Vision (ECCV), 300-316, 2018 | 74 | 2018 |
| Small-scale pedestrian detection based on somatic topology localization and temporal feature aggregation T Song, L Sun, D Xie, H Sun, S Pu arXiv preprint arXiv:1807.01438, 2018 | 70 | 2018 |
| Few-shot class-incremental learning by sampling multi-phase tasks DW Zhou, HJ Ye, L Ma, D Xie, S Pu, DC Zhan IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 65 | 2022 |
| Label matching semi-supervised object detection B Chen, W Chen, S Yang, Y Xuan, J Song, D Xie, S Pu, M Song, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 60 | 2022 |
| 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 | 60 | 2020 |
| Learning incremental triplet margin for person re-identification Y Zhang, Q Zhong, L Ma, D Xie, S Pu Proceedings of the AAAI conference on artificial intelligence 33 (01), 9243-9250, 2019 | 50 | 2019 |
| Self-supervised noisy label learning for source-free unsupervised domain adaptation W Chen, L Lin, S Yang, D Xie, S Pu, Y Zhuang 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 49 | 2022 |
