| Action recognition from depth maps using deep convolutional neural networks P Wang, W Li, Z Gao, J Zhang, C Tang, PO Ogunbona IEEE transactions on human-machine systems 46 (4), 498-509, 2015 | 357 | 2015 |
| HEp-2 cell image classification with deep convolutional neural networks Z Gao, L Wang, L Zhou, J Zhang IEEE journal of biomedical and health informatics 21 (2), 416-428, 2016 | 280 | 2016 |
| Depth pooling based large-scale 3-d action recognition with convolutional neural networks P Wang, W Li, Z Gao, C Tang, PO Ogunbona IEEE Transactions on Multimedia 20 (5), 1051-1061, 2018 | 196 | 2018 |
| MDSSD: multi-scale deconvolutional single shot detector for small objects L Cui, R Ma, P Lv, X Jiang, Z Gao, B Zhou, M Xu arXiv preprint arXiv:1805.07009, 2018 | 192 | 2018 |
| Scene flow to action map: A new representation for rgb-d based action recognition with convolutional neural networks P Wang, W Li, Z Gao, Y Zhang, C Tang, P Ogunbona Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 183 | 2017 |
| Trear: Transformer-based RGB-D Egocentric Action Recognition X Li, Y Hou, P Wang, Z Gao, M Xu, W Li IEEE Transactions on Cognitive and Developmental Systems, 1-7, 2021 | 126 | 2021 |
| Large-scale isolated gesture recognition using convolutional neural networks P Wang, W Li, S Liu, Z Gao, C Tang, P Ogunbona 2016 23rd international conference on pattern recognition (ICPR), 7-12, 2016 | 120 | 2016 |
| Convnets-based action recognition from depth maps through virtual cameras and pseudocoloring P Wang, W Li, Z Gao, C Tang, J Zhang, P Ogunbona Proceedings of the 23rd ACM international conference on Multimedia, 1119-1122, 2015 | 120 | 2015 |
| Context-aware block net for small object detection L Cui, P Lv, X Jiang, Z Gao, B Zhou, L Zhang, L Shao, M Xu IEEE Transactions on cybernetics 52 (4), 2300-2313, 2020 | 108 | 2020 |
| Structured images for RGB-D action recognition P Wang, S Wang, Z Gao, Y Hou, W Li Proceedings of the IEEE international conference on computer vision …, 2017 | 74 | 2017 |
| Deep convolutional neural networks for action recognition using depth map sequences P Wang, W Li, Z Gao, J Zhang, C Tang, P Ogunbona arXiv preprint arXiv:1501.04686, 2015 | 71 | 2015 |
| Focal and global spatial-temporal transformer for skeleton-based action recognition Z Gao, P Wang, P Lv, X Jiang, Q Liu, P Wang, M Xu, W Li Proceedings of the Asian Conference on Computer Vision, 382-398, 2022 | 70 | 2022 |
| Large-scale continuous gesture recognition using convolutional neural networks P Wang, W Li, S Liu, Y Zhang, Z Gao, P Ogunbona 2016 23rd International Conference on Pattern Recognition (ICPR), 13-18, 2016 | 70 | 2016 |
| A central difference graph convolutional operator for skeleton-based action recognition S Miao, Y Hou, Z Gao, M Xu, W Li IEEE Transactions on Circuits and Systems for Video Technology 32 (7), 4893-4899, 2021 | 64 | 2021 |
| Mining mid-level features for action recognition based on effective skeleton representation P Wang, W Li, P Ogunbona, Z Gao, H Zhang 2014 International Conference on Digital Image Computing: Techniques and …, 2014 | 57 | 2014 |
| Hep-2 cell image classification with convolutional neural networks Z Gao, J Zhang, L Zhou, L Wang 2014 1st Workshop on Pattern Recognition Techniques for Indirect …, 2014 | 54 | 2014 |
| Transformer guided geometry model for flow-based unsupervised visual odometry X Li, Y Hou, P Wang, Z Gao, M Xu, W Li Neural Computing and Applications, 1-12, 2021 | 36 | 2021 |
| Human pose-based estimation, tracking and action recognition with deep learning: A survey L Zhou, X Meng, Z Liu, M Wu, Z Gao, P Wang arXiv preprint arXiv:2310.13039, 2023 | 32 | 2023 |
| A review of dynamic maps for 3D human motion recognition using ConvNets and its improvement Z Gao, P Wang, H Wang, M Xu, W Li Neural Processing Letters 52 (2), 1501-1515, 2020 | 29 | 2020 |
| Spatially and temporally structured global to local aggregation of dynamic depth information for action recognition Y Hou, S Wang, P Wang, Z Gao, W Li IEEE Access 6, 2206-2219, 2017 | 22 | 2017 |
Pichao WANGNvidiaVerified email at nvidia.com
