| Bayesian loss for crowd count estimation with point supervision Z Ma, X Wei, X Hong, Y Gong Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 567 | 2019 |
| Transductive semi-supervised deep learning using min-max features W Shi, Y Gong, C Ding, Z Ma, X Tao, N Zheng Proceedings of the European Conference on Computer Vision (ECCV), 299-315, 2018 | 268 | 2018 |
| Boosting crowd counting via multifaceted attention H Lin, Z Ma, R Ji, Y Wang, X Hong Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 154 | 2022 |
| Learning to Count via Unbalanced Optimal Transport Z Ma, X Wei, X Hong, H Lin, Y Qiu, Y Gong Proceedings of the AAAI Conference on Artificial Intelligence, 2021 | 80 | 2021 |
| Superpixel Masking and Inpainting for Self-Supervised Anomaly Detection Z Li, N Li, K Jiang, Z Ma, X Wei, X Hong, Y Gong 31th British Machine Vision Conference (BMVC), 2020 | 71 | 2020 |
| Towards a universal model for cross-dataset crowd counting Z Ma, X Hong, X Wei, Y Qiu, Y Gong Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 49 | 2021 |
| Direct measure matching for crowd counting H Lin, X Hong, Z Ma, X Wei, Y Qiu, Y Wang, Y Gong Proceedings of the Thirtieth International Joint Conference on Artificial …, 2021 | 42 | 2021 |
| Learning scales from points: A scale-aware probabilistic model for crowd counting Z Ma, X Wei, X Hong, Y Gong Proceedings of the 28th ACM International Conference on Multimedia, 220-228, 2020 | 41 | 2020 |
| Transductive semi-supervised metric learning for person re-identification X Chang, Z Ma, X Wei, X Hong, Y Gong Pattern Recognition 108, 107569, 2020 | 37 | 2020 |
| Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting Y He, Z Ma, X Wei, X Hong, W Ke, Y Gong Proceedings of the AAAI Conference on Artificial Intelligence, 2021 | 30 | 2021 |
| Anomaly detection via self-organizing map N Li, K Jiang, Z Ma, X Wei, X Hong, Y Gong 2021 IEEE International Conference on Image Processing (ICIP), 974-978, 2021 | 25 | 2021 |
| Isolation and impartial aggregation: A paradigm of incremental learning without interference Y Wang, Z Ma, Z Huang, Y Wang, Z Su, X Hong Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 10209 …, 2023 | 23 | 2023 |
| Semi-supervised crowd counting via density agency H Lin, Z Ma, X Hong, Y Wang, Z Su Proceedings of the 30th ACM International Conference on Multimedia, 1416-1426, 2022 | 22 | 2022 |
| Eccnas: Efficient crowd counting neural architecture search Y Wang, Z Ma, X Wei, S Zheng, Y Wang, X Hong ACM Transactions on Multimedia Computing, Communications, and Applications …, 2022 | 21 | 2022 |
| Can sam count anything? an empirical study on sam counting Z Ma, X Hong, Q Shangguan arXiv preprint arXiv:2304.10817, 2023 | 20 | 2023 |
| Sparse parameterization for epitomic dataset distillation X Wei, A Cao, F Yang, Z Ma Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Topology-preserving transfer learning for weakly-supervised anomaly detection and segmentation S Wei, X Wei, MR Kurniawan, Z Ma, Y Gong Pattern Recognition Letters 170, 77-84, 2023 | 4 | 2023 |
| Evolving Parameterized Prompt Memory for Continual Learning MR Kurniawan, X Song, Z Ma, Y He, Y Gong, Y Qi, X Wei Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13301 …, 2024 | 3 | 2024 |
| Semi-Supervised Crowd Counting via Multiple Representation Learning X Wei, Y Qiu, Z Ma, X Hong, Y Gong IEEE Transactions on Image Processing, 2023 | 3 | 2023 |
| Gramformer: Learning Crowd Counting via Graph-Modulated Transformer H Lin, Z Ma, X Hong, Q Shangguan, D Meng Proceedings of the AAAI Conference on Artificial Intelligence 38 (4), 3395-3403, 2024 | 2 | 2024 |
Xiaopeng HONG (洪晓鹏)Professor, Harbin Institute of TechnologyVerified email at ieee.org
