| 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 | 591 | 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, 2020 | 375 | 2020 |
| Embedding structured contour and location prior in siamesed fully convolutional networks for road detection J Gao, Q Wang, Y Yuan Robotics and Automation (ICRA), 2017 IEEE International Conference on, 219-224, 2017 | 280 | 2017 |
| Pcc net: Perspective crowd counting via spatial convolutional network J Gao, Q Wang, X Li IEEE Transactions on Circuits and Systems for Video Technology 30 (10), 3486 …, 2019 | 223 | 2019 |
| Weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes Q Wang, J Gao, X Li IEEE Transactions on Image Processing 28 (9), 4376-4386, 2019 | 202 | 2019 |
| Cnn-based density estimation and crowd counting: A survey G Gao, J Gao, Q Liu, Q Wang, Y Wang arXiv preprint arXiv:2003.12783, 2020 | 195 | 2020 |
| SCAR: Spatial-/channel-wise attention regression networks for crowd counting J Gao, Q Wang, Y Yuan Neurocomputing 363, 1-8, 2019 | 193 | 2019 |
| A joint convolutional neural networks and context transfer for street scenes labeling Q Wang, J Gao, Y Yuan IEEE Transactions on Intelligent Transportation Systems 19 (5), 1457-1470, 2018 | 160 | 2018 |
| 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 | 109 | 2021 |
| 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 | 102 | 2019 |
| Domain-Adaptive Crowd Counting via High-Quality Image Translation and Density Reconstruction J Gao, T Han, Y Yuan, Q Wang IEEE transactions on neural networks and learning systems, 2021 | 85* | 2021 |
| Feature-Aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance J Gao, Y Yuan, Q Wang IEEE Transactions on Cybernetics, 2020 | 78* | 2020 |
| Neuron linear transformation: Modeling the domain shift for crowd counting Q Wang, T Han, J Gao, Y Yuan IEEE Transactions on Neural Networks and Learning Systems 33 (8), 3238-3250, 2021 | 72 | 2021 |
| Multitask attention network for lane detection and fitting Q Wang, T Han, Z Qin, J Gao, X Li IEEE transactions on neural networks and learning systems 33 (3), 1066-1078, 2020 | 60 | 2020 |
| Focus on semantic consistency for cross-domain crowd understanding T Han, J Gao, Y Yuan, Q Wang ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 52 | 2020 |
| Congested crowd instance localization with dilated convolutional swin transformer J Gao, M Gong, X Li Neurocomputing 513, 94-103, 2022 | 36 | 2022 |
| Learning to detect anomaly events in crowd scenes from synthetic data W Lin, J Gao, Q Wang, X Li Neurocomputing 436, 248-259, 2021 | 36 | 2021 |
| Learning independent instance maps for crowd localization J Gao, T Han, Q Wang, Y Yuan, X Li arXiv preprint arXiv:2012.04164, 2020 | 35 | 2020 |
| Ambient sound helps: Audiovisual crowd counting in extreme conditions D Hu, L Mou, Q Wang, J Gao, Y Hua, D Dou, XX Zhu arXiv preprint arXiv:2005.07097, 2020 | 33 | 2020 |
| Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning T Han, J Gao, Y Yuan, Q Wang Advances in Neural Information Processing Systems 33, 2020 | 27 | 2020 |
Qi WangNorthwestern Polytechnical UniversityVerified email at nwpu.edu.cn
