| Generative adversarial networks in computer vision: A survey and taxonomy Z Wang, Q She, TE Ward ACM Computing Surveys (CSUR) 54 (2), 1-38, 2021 | 696* | 2021 |
| Involution: Inverting the Inherence of Convolution for Visual Recognition D Li, J Hu, C Wang, X Li, Q She, L Zhu, T Zhang, Q Chen CVPR 2021, 2021 | 389 | 2021 |
| NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction Y Gao, Q She, J Ma, M Zhao, W Liu, AL Yuille CVPR 2019, 3205-3214, 2019 | 311 | 2019 |
| Generative adversarial networks in time series: A systematic literature review E Brophy, Z Wang, Q She, T Ward ACM Computing Surveys 55 (10), 1-31, 2023 | 235* | 2023 |
| ACTION-Net: Multipath Excitation for Action Recognition Z Wang, Q She, A Smolic CVPR 2021, 2021 | 217 | 2021 |
| Avalanche: an End-to-End Library for Continual Learning V Lomonaco, L Pellegrini, A Cossu, A Carta, G Graffieti, TL Hayes, ... CVPR 2021 Continual Learning Workshop, 2021 | 198 | 2021 |
| Mine: Towards Continuous Depth MPI with Nerf for Novel View Synthesis J Li, Z Feng, Q She, H Ding, C Wang, GH Lee ICCV 2021, 12578-12588, 2021 | 149 | 2021 |
| Are we ready for service robots? The OpenLORIS-scene datasets for lifelong SLAM X Shi, D Li, P Zhao, Q Tian, Y Tian, Q Long, C Zhu, J Song, F Qiao, ... ICRA 2020, 3139-3145, 2020 | 146 | 2020 |
| OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning Q She, F Feng, X Hao, Q Yang, C Lan, V Lomonaco, X Shi, Z Wang, ... ICRA 2020, 4767-4773, 2020 | 135* | 2020 |
| Stochastic Trajectory Prediction with Social Graph Network L Zhang, Q She, P Guo arXiv preprint arXiv:1907.10233, 2019 | 79 | 2019 |
| Learning from Temporal Gradient for Semi-supervised Action Recognition J Xiao, L Jing, L Zhang, J He, Q She, Z Zhou, A Yuille, Y Li CVPR 2022, 2022 | 70 | 2022 |
| CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions V Lomonaco, L Pellegrini, P Rodriguez, M Caccia, Q She, Y Chen, ... Artificial Intelligence, 2022, 2022 | 60 | 2022 |
| On Learning Contrastive Representations for Learning with Noisy Labels L Yi, S Liu, Q She, AI McLeod, B Wang CVPR 2022, 2022 | 57 | 2022 |
| Challenges in task incremental learning for assistive robotics F Feng, RHM Chan, X Shi, Y Zhang, Q She IEEE Access 8, 3434-3441, 2019 | 44 | 2019 |
| Neural dynamics discovery via gaussian process recurrent neural networks Q She, A Wu Uncertainty in Artificial Intelligence (UAI), 454-464, 2020 | 42 | 2020 |
| Learning the Superpixel in a Non-iterative and Lifelong Manner L Zhu, Q She, B Zhang, Y Lu, Z Lu, D Li, J Hu CVPR 2021, 2021 | 37 | 2021 |
| Weakly Supervised Object Localization as Domain Adaption L Zhu, Q She, Q Chen, Y You, B Wang, Y Lu CVPR 2022, 2022 | 35 | 2022 |
| Unifying Nonlocal Blocks for Neural Networks L Zhu, Q She, D Li, Y Lu, X Kang, J Hu, C Wang ICCV 2021, 2021 | 27 | 2021 |
| Evaluating the small-world-ness of a sampled network: Functional connectivity of entorhinal-hippocampal circuitry Q She, G Chen, RHM Chan Nature Scientific Reports 6, 21468, 2016 | 27 | 2016 |
| Reduced-Rank Linear Dynamical Systems Q She, Y Gao, R Xu, Kai；Chan AAAI 2018, 4050-4057, 2018 | 21 | 2018 |
Zhengwei WangByteDanceVerified email at tcd.ie
