| Learning from synthetic data for crowd counting in the wild Q Wang, J Gao, W Lin, Y Yuan Proceedings of the IEEE/CVF Conference on computer vision and pattern …, 2019 | 323 | 2019 |
| NWPU-crowd: A large-scale benchmark for crowd counting and localization Q Wang, J Gao, W Lin, X Li IEEE transactions on pattern analysis and machine intelligence 43 (6), 2141-2149, 2020 | 155 | 2020 |
| C^ 3 framework: An open-source pytorch code for crowd counting J Gao, W Lin, B Zhao, D Wang, C Gao, J Wen arXiv preprint arXiv:1907.02724, 2019 | 59 | 2019 |
| Pixel-wise crowd understanding via synthetic data Q Wang, J Gao, W Lin, Y Yuan International Journal of Computer Vision 129 (1), 225-245, 2021 | 48 | 2021 |
| Learning to detect anomaly events in crowd scenes from synthetic data W Lin, J Gao, Q Wang, X Li Neurocomputing 436, 248-259, 2021 | 11 | 2021 |
| Density-aware curriculum learning for crowd counting Q Wang, W Lin, J Gao, X Li IEEE Transactions on Cybernetics, 2020 | 10 | 2020 |
| Cross-View Cross-Scene Multi-View Crowd Counting Q Zhang, W Lin, AB Chan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 7 | 2021 |
| Pixel-Level Self-Paced Learning For Super-Resolution W Lin, J Gao, Q Wang, X Li ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 2 | 2020 |
| Cross-View Cross-Scene Multi-View Crowd Counting Supplemental Q Zhang, W Lin, AB Chan | | |
Junyu GaoXidian UniversityVerified email at mail.nwpu.edu.cn
