most citedA Model-driven Deep Neural Network for Single Image Rain Removal

28 citations · 95 across the 8 of their papers we have counts for

collaborators

9 papers

eess.IV20211 cited

A Deep Variational Bayesian Framework for Blind Image Deblurring

Hui Wang, Zongsheng Yue, Qian Zhao +1

Blind image deblurring is an important yet very challenging problem in low-level vision. Traditional optimization based methods generally formulate this task as a maximum-a-posteri…

cs.CV20214 cited

Semi-Supervised Video Deraining with Dynamical Rain Generator

Zongsheng Yue, Jianwen Xie, Qian Zhao +1

While deep learning (DL)-based video deraining methods have achieved significant success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently m…

cs.CV20207 cited

Meta Feature Modulator for Long-tailed Recognition

Renzhen Wang, Kaiqin Hu, Yanwen Zhu +3

Deep neural networks often degrade significantly when training data suffer from class imbalance problems. Existing approaches, e.g., re-sampling and re-weighting, commonly address…

cs.CV2020

From Rain Generation to Rain Removal

Hong Wang, Zongsheng Yue, Qi Xie +3

For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most o…

cs.CV202014 cited

Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation

Zongsheng Yue, Qian Zhao, Lei Zhang +1

Real-world image noise removal is a long-standing yet very challenging task in computer vision. The success of deep neural network in denoising stimulates the research of noise gen…

cs.LG202022 cited

Meta Transition Adaptation for Robust Deep Learning with Noisy Labels

Jun Shu, Qian Zhao, Zongben Xu +1

To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted la…