| Deep stacked hierarchical multi-patch network for image deblurring H Zhang, Y Dai, H Li, P Koniusz Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 659 | 2019 |
| Adaptive Subspaces for Few-Shot Learning C Simon, P Koniusz, R Nock, M Harandi Computer Vision and Pattern Recognition (CVPR), 2020 | 491 | 2020 |
| Convolutional Kernel Networks J Mairal, P Koniusz, Z Harchaoui, C Schmid Advances Neural Information Processing Systems (NIPS), 2014 | 483 | 2014 |
| Simple Spectral Graph Convolution H Zhu, P Koniusz International Conference on Learning Representations (ICLR), 2021 | 348 | 2021 |
| A Comparative Review of Recent Kinect-basedAction Recognition Algorithms L Wang, DQ Huynh, P Koniusz IEEE Transactions on Image Processing (TIP), 2019 | 269 | 2019 |
| Few-Shot Learning via Saliency-guided Hallucination of Samples H Zhang, J Zhang, P Koniusz IEEE Computer Vision and Pattern Recognition (CVPR), 2019 | 268 | 2019 |
| Few-shot Action Recognition with Permutation-invariant Attention H Zhang, L Zhang, X Qi, H Li, PHS Torr, P Koniusz European Conference on Computer Vision (ECCV), 2020 | 197 | 2020 |
| Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors P Koniusz, Y Tas, F Porikli Computer Vision and Pattern Recognition, 2017 | 156 | 2017 |
| Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons P Koniusz, A Cherian, F Porikli European Conference on Computer Vision, 2016 | 152 | 2016 |
| On Learning the Geodesic Path for Incremental Learning C Simon, P Koniusz, M Harandi Computer Vision and Pattern Recognition (CVPR), 2021 | 135 | 2021 |
| Higher-order occurrence pooling for bags-of-words: Visual concept detection P Koniusz, F Yan, PH Gosselin, K Mikolajczyk IEEE transactions on pattern analysis and machine intelligence 39 (2), 313-326, 2017 | 133 | 2017 |
| Zero-Shot Kernel Learning H Zhang, P Koniusz The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7670-7679, 2018 | 125 | 2018 |
| COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning Y Zhang, H Zhu, Z Song, P Koniusz, I King International Conference on Knowledge Discovery and Data Mining (KDD 2022), 2022 | 122 | 2022 |
| Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs L Wang, P Koniusz, DQ Huynh International Conference on Computer Vision (ICCV), 2019 | 119 | 2019 |
| Comparison of mid-level feature coding approaches and pooling strategies in visual concept detection P Koniusz, F Yan, K Mikolajczyk Computer vision and image understanding 117 (5), 479-492, 2013 | 104 | 2013 |
| Spectral Feature Augmentation for Graph Contrastive Learning and Beyond Y Zhang, H Zhu, Z Song, P Koniusz, I King International Conference on Artificial Intelligence (AAAI), 2023, arXiv …, 2022 | 82 | 2022 |
| Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning H Zhang, P Koniusz, S Jian, H Li, PHS Torr Computer Vision and Pattern Recognition (CVPR), 2021 | 73 | 2021 |
| Tensor Representations for Action Recognition P Koniusz, L Wang, A Cherian IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 | 70 | 2020 |
| Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification P Koniusz, H Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 | 69 | 2020 |
| Power Normalizing Second-order Similarity Network for Few-shot Learning H Zhang, P Koniusz IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 | 68 | 2019 |
Hongguang ZhangAustralian National University, Data61/CSIRO在 alumni.anu.edu.au 的电子邮件经过验证
