| Diffusion probabilistic models for 3d point cloud generation S Luo, W Hu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 633 | 2021 |
| RGCNN: Regularized Graph CNN for Point Cloud Segmentation G Te, W Hu, Z Guo, A Zheng ACM Multimedia, 2018 | 363 | 2018 |
| Optimized skeleton-based action recognition via sparsified graph regression X Gao, W Hu, J Tang, J Liu, Z Guo Proceedings of the 27th ACM International Conference on Multimedia, 601-610, 2019 | 242* | 2019 |
| Multi-resolution Graph Fourier Transform for Compression of Piecewise Smooth Images W Hu, G Cheung, A Ortega, OC Au Transactions on Image Processing 24 (1), 419-433, 2015 | 217 | 2015 |
| Score-based point cloud denoising S Luo, W Hu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 159 | 2021 |
| Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries Q Hu, X Wang, W Hu, GJ Qi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 159 | 2021 |
| Multiscale geographically and temporally weighted regression: Exploring the spatiotemporal determinants of housing prices C Wu, F Ren, W Hu, Q Du International Journal of Geographical Information Science 33 (3), 489-511, 2019 | 127 | 2019 |
| Differentiable manifold reconstruction for point cloud denoising S Luo, W Hu Proceedings of the 28th ACM international conference on multimedia, 1330-1338, 2020 | 114 | 2020 |
| Depth map denoising using graph-based transform and group sparsity W Hu, X Li, G Cheung, O Au 2013 IEEE 15th international workshop on multimedia signal processing (MMSP …, 2013 | 107 | 2013 |
| Feature Graph Learning for 3D Point Cloud Denoising W Hu, X Gao, G Cheung, Z Guo Transactions on Signal Processing, 2020 | 94 | 2020 |
| Self-contrastive learning with hard negative sampling for self-supervised point cloud learning B Du, X Gao, W Hu, X Li Proceedings of the 29th ACM International Conference on Multimedia, 3133-3142, 2021 | 83 | 2021 |
| Intra-Prediction and Generalized Graph Fourier Transform for Image Coding W Hu, G Cheung, A Ortega IEEE Signal Processing Letters 22 (11), 1913 - 1917, 2015 | 79 | 2015 |
| Exploring structure-adaptive graph learning for robust semi-supervised classification X Gao, W Hu, Z Guo 2020 ieee international conference on multimedia and expo (icme), 1-6, 2020 | 75 | 2020 |
| Edge-aware graph representation learning and reasoning for face parsing G Te, Y Liu, W Hu, H Shi, T Mei Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 72 | 2020 |
| Graph Signal Processing for Geometric Data and Beyond: Theory and Applications W Hu, J Pang, X Liu, D Tian, CW Lin, A Vetro arXiv preprint arXiv:2008.01918, 2020 | 70 | 2020 |
| Depth map compression using multi-resolution graph-based transform for depth-image-based rendering W Hu, G Cheung, X Li, O Au 2012 19th IEEE International Conference on Image Processing, 1297-1300, 2012 | 70 | 2012 |
| Local frequency interpretation and non-local self-similarity on graph for point cloud inpainting W Hu, Z Fu, Z Guo IEEE Transactions on Image Processing 28 (8), 4087-4100, 2019 | 69 | 2019 |
| Non-local graph convolutional networks for skeleton-based action recognition L Shi, Y Zhang, J Cheng, H Lu arXiv preprint arXiv:1805.07694 1 (2), 3, 2018 | 64 | 2018 |
| Predictive generalized graph Fourier transform for attribute compression of dynamic point clouds Y Xu, W Hu, S Wang, X Zhang, S Wang, S Ma, Z Guo, W Gao IEEE Transactions on Circuits and Systems for Video Technology 31 (5), 1968-1982, 2020 | 61 | 2020 |
| Graph-based Dequantization of Block-Compressed Piecewise Smooth Images MK Wei Hu, Gene Cheung IEEE Signal Processing Letters 23 (2), 242-246, 2016 | 55* | 2016 |
Gene CheungProfessor, Department of EECS, York UniversityVerified email at yorku.ca
