| Action recognition from depth maps using deep convolutional neural networks P Wang, W Li, Z Gao, J Zhang, C Tang, PO Ogunbona Human-Machine Systems, IEEE Transactions on 46 (4), 498-509, 2016 | 251 | 2016 |
| Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks P Wang, Z Li, Y Hou, W Li Proceedings of the 2016 ACM on Multimedia Conference, 2016, 102-106, 2016 | 240 | 2016 |
| Skeleton Optical Spectra Based Action Recognition Using Convolutional Neural Networks Y Hou, Z Li, P Wang, W Li IEEE Transactions on Circuits and Systems for Video Technology 28 (3), 807 - 811, 2018 | 217 | 2018 |
| RGB-D-based Action Recognition Datasets: A Survey J Zhang, W Li, PO Ogunbona, P Wang, C Tang Pattern Recognition 60, 86-105, 2016 | 209 | 2016 |
| RGB-D-based human motion recognition with deep learning: A survey P Wang, W Li, P Ogunbona, J Wan, S Escalera Computer Vision and Image Understanding 171, 118-139, 2018 | 208 | 2018 |
| Joint distance maps based action recognition with convolutional neural networks C Li, Y Hou, P Wang, W Li IEEE Signal Processing Letters 24 (5), 624-628, 2017 | 161 | 2017 |
| Skeleton-based action recognition using LSTM and CNN C Li, P Wang, S Wang, Y Hou, W Li 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW …, 2017 | 108 | 2017 |
| 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 | 99 | 2015 |
| 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 Recognition, 2017 | 96 | 2017 |
| Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks P Wang, W Li, C Li, Y Hou Knowledge-Based Systems 158, 43-53, 2018 | 93 | 2018 |
| Large-scale isolated gesture recognition using convolutional neural networks P Wang, W Li, S Liu, Z Gao, C Tang, P Ogunbona Pattern Recognition (ICPR), 2016 23rd International Conference on, 2016 | 80 | 2016 |
| Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks P Wang, W Li, Z Gao, C Tang, P Ogunbona IEEE Transactions on Multimedia 20 (5), 1051 - 1061, 2018 | 71 | 2018 |
| Learning a joint affinity graph for multiview subspace clustering C Tang, X Zhu, X Liu, M Li, P Wang, C Zhang, L Wang IEEE Transactions on Multimedia 21 (7), 1724-1736, 2018 | 69 | 2018 |
| A spectral and spatial approach of coarse-to-fine blurred image region detection C Tang, J Wu, Y Hou, P Wang, W Li IEEE Signal Processing Letters 23 (11), 1652-1656, 2016 | 58 | 2016 |
| Large-scale multimodal gesture recognition using heterogeneous networks H Wang, P Wang, Z Song, W Li Proceedings of the IEEE International Conference on Computer Vision, 3129-3137, 2017 | 53 | 2017 |
| Salient Object Detection via Weighted Low Rank Matrix Recovery C Tang, J Wu, C Zhang, P Wang, W Li IEEE Signal Processing Letters, 2016 | 53 | 2016 |
| 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 | 51 | 2015 |
| Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition P Wang, W Li, J Wan, P Ogunbona, X Liu AAAI, 2018 | 50 | 2018 |
| Investigation of different skeleton features for cnn-based 3d action recognition Z Ding, P Wang, PO Ogunbona, W Li 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW …, 2017 | 48 | 2017 |
| 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 | 47 | 2014 |
Wanqing LiUniversity of Wollongong在 uow.edu.au 的电子邮件经过验证
