| Joint Geometrical and Statistical Alignment for Visual Domain Adaptation J Zhang, W Li, P Ogunbona IEEE Conference on Computer Vision and Pattern Recognition, 2017 | 661 | 2017 |
| Importance Weighted Adversarial Nets for Partial Domain Adaptation J Zhang, Z Ding, W Li, P Ogunbona IEEE Conference on Computer Vision and Pattern Recognition, 2018 | 523 | 2018 |
| 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 | 354 | 2015 |
| RGB-D-based action recognition datasets: A survey J Zhang, W Li, PO Ogunbona, P Wang, C Tang Pattern Recognition 60, 86-105, 2016 | 309 | 2016 |
| Cross-view locality preserved diversity and consensus learning for multi-view unsupervised feature selection C Tang, X Zheng, X Liu, W Zhang, J Zhang, J Xiong, L Wang IEEE Transactions on Knowledge and Data Engineering 34 (10), 4705-4716, 2021 | 186 | 2021 |
| Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective J Zhang, W Li, P Ogunbona, D Xu ACM Computing Surveys (CSUR) 52 (1), 1-38, 2019 | 179 | 2019 |
| 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 | 117 | 2015 |
| 3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds D Cai, L Zhao, J Zhang*, L Sheng, D Xu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 111 | 2022 |
| VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation J Tian, J Zhang*, W Li, D Xu IEEE Transactions on Circuits and Systems for Video Technology, 2021 | 99 | 2021 |
| 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 | 70 | 2015 |
| Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors C Tang, W Li, C Hou, P Wang, Y Hou, J Zhang, PO Ogunbona arXiv preprint arXiv:1511.03028, 2015 | 55* | 2015 |
| Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter J Wang, X Li, J Zhang*, Q Xu, Q Zhou, Q Yu, L Sheng, D Xu arXiv preprint arXiv:2309.02773, 2023, 2023 | 43 | 2023 |
| Source Data-free Unsupervised Domain Adaptation for Semantic Segmentation M Ye, J Zhang*, J Ouyang, D Yuan 29th ACM International Conference on Multimedia, 2021 | 42 | 2021 |
| DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models X Xing, C Wang, H Zhou, J Zhang, Q Yu, D Xu Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 39 | 2023 |
| Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline L Zhao, D Cai, J Zhang*, L Sheng, D Xu, R Zheng, Y Zhao, L Wang, X Fan IEEE Transactions on Circuits and Systems for Video Technology, 2022 | 27 | 2022 |
| A Large Scale RGB-D Dataset for Action Recognition J Zhang, W Li, P Wang, P Ogunbona, S Liu, C Tang UHA3DS in International Conference on Pattern Recognition, 2016 | 27 | 2016 |
| Unsupervised domain adaptation: A multi-task learning-based method J Zhang, W Li, P Ogunbona Knowledge-Based Systems, 2019 | 24 | 2019 |
| Progressive Modality Cooperation for Multi-Modality Domain Adaptation W Zhang, D Xu, J Zhang, W Ouyang IEEE Transactions on Image Processing, 2021 | 23 | 2021 |
| SVGDreamer: Text Guided SVG Generation with Diffusion Model X Xing, H Zhou, C Wang, J Zhang, D Xu, Q Yu IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 | 22 | 2024 |
| Improving rgb-d point cloud registration by learning multi-scale local linear transformation Z Wang, X Huo, Z Chen, J Zhang, L Sheng, D Xu European Conference on Computer Vision, 175-191, 2022 | 22 | 2022 |
Wanqing LiProfessor, University of WollongongVerifisert e-postadresse på uow.edu.au