| Rethinking feature distribution for loss functions in image classification W Wan*, Y Zhong*, T Li, J Chen Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 180 | 2018 |
| Pixel contrastive-consistent semi-supervised semantic segmentation Y Zhong, B Yuan, H Wu, Z Yuan, J Peng, YX Wang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 155 | 2021 |
| Anchor box optimization for object detection Y Zhong, J Wang, J Peng, L Zhang Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 103 | 2020 |
| Toward end-to-end face recognition through alignment learning Y Zhong, J Chen, B Huang IEEE signal processing letters 24 (8), 1213-1217, 2017 | 76 | 2017 |
| Boosting weakly supervised object detection with progressive knowledge transfer Y Zhong, J Wang, J Peng, L Zhang European conference on computer vision, 615-631, 2020 | 50 | 2020 |
| Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning Y Liu, Y Luo, Y Zhong, X Chen, Q Liu, J Peng arXiv preprint arXiv:1905.13420, 2019 | 40 | 2019 |
| Structinf: Mining structural influence from social streams J Zhang, J Tang, Y Zhong, Y Mo, J Li, G Song, W Hall, J Sun Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 37 | 2017 |
| Is self-supervised learning more robust than supervised learning? Y Zhong, H Tang, J Chen, J Peng, YX Wang arXiv preprint arXiv:2206.05259, 2022 | 28* | 2022 |
| DAP: Detection-Aware Pre-training with Weak Supervision Y Zhong, J Wang, L Wang, J Peng, YX Wang, L Zhang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 20 | 2021 |
| Do pre-trained models benefit equally in continual learning? KY Lee, Y Zhong, YX Wang Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 11 | 2023 |
| Shaping Deep Feature Space Towards Gaussian Mixture for Visual Classification W Wan, J Chen, C Yu, T Wu, Y Zhong, MH Yang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 8 | 2022 |
| Disentangling controllable object through video prediction improves visual reinforcement learning Y Zhong, A Schwing, J Peng ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 6 | 2020 |
| Efficient competitive self-play policy optimization Y Zhong, Y Zhou, J Peng NeurIPS 2020 Workshop on Deep Reinforcement Learning, 2020 | 4 | 2020 |
| Sirfyn: Single image relighting from your neighbors DA Forsyth, A Bhattad, P Asthana, Y Zhong, Y Wang arXiv preprint arXiv:2112.04497, 2021 | 2 | 2021 |
| Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study Y Zhong, H Tang, JK Chen, YX Wang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 1 | 2023 |
| Improving equivariance in state-of-the-art supervised depth and normal predictors Y Zhong, A Bhattad, YX Wang, D Forsyth Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 1 | 2023 |
| Coordinate-wise control variates for deep policy gradients Y Zhong, Y Zhou, J Peng ICML 2021 Workshop on Reinforcement Learning for Real Life, 2021 | 1 | 2021 |
| YouTubePD: A Multimodal Benchmark for Parkinson’s Disease Analysis A Zhou, S Li, P Sriram, X Li, J Dong, A Sharma, Y Zhong, S Luo, ... Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Sample-efficient learning with self-supervision Y Zhong | | 2022 |
| Supplementary Material Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study Y Zhong, H Tang, JK Chen, YX Wang | | |
