Zhang Yi

I am currently a fourth-year Ph.D. student at MMLab under the supervision of Prof. Hongsheng Li and Prof. Xiaogang Wang. Before joining CUHK, I received my bachelor's degree from Nanjing University in 2019.

Email / Google Scholar / Github / CV

Research

My research interests include computer vision, computational photography, and generative models. I enjoy the research that can benefit real-world applications. Most of my papers have been used in some commercial products (e.g. XIAOMI's flagship smartphones).

	KBNet: Kernel Basis Network for Image Restoration Yi Zhang, Dasong Li, Xiaoyu Shi, Dailan He, Kangning Song, Xiaogang Wang, Honwei Qin, Hongsheng Li arXiv A general-purpose backbone for image restoration tasks (e.g. denoising, deraining, and deblurring). A novel kernel basis attention (KBA) module has been proposed to effectively aggregate the spatial information via a series of learnable kernel bases.
	No Attention is Needed: Grouped Spatial-temporal Shift for Simple and Efficient Video Restorers Dasong Li, Xiaoyu Shi, Yi Zhang, Xiaogang Wang, Honwei Qin, Hongsheng Li arXiv, 2022 arXiv / code / project We propose a grouped spatial-temporal shift module for effective video restoration. It surpasses previous SOTA methods with only 43% computational cost on video deblurring and denoising.
	Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network Dasong Li, Yi Zhang, Ka Lung Law, Xiaogang Wang, Honwei Qin, Hongsheng Li IJCV, 2022 arXiv A practical low-light imaging system for burst raw image denoising. It has been deployed to some commercial smartphones.
	Learning Degradation Representations for Image Deblurring Dasong Li, Yi Zhang, Ka Chun Cheung, Xiaogang Wang, Honwei Qin, Hongsheng Li ECCV, 2022 arXiv / code Learning degradation representations for blurry images. The learned degradation representation shows pleasing improvements on both image deblurring and reblurring.
	Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang, Dasong Li, Ka Lung Law, Xiaogang Wang, Honwei Qin, Hongsheng Li CVPR, 2022 arXiv / code / SenseNoise dataset A self-supervised image denoising method using noisy images and the noise model. We achieve SOTA results on both real-world noise and synthetic noises (both point-wise and spatial correlated noise types).
	Rethinking Noise Synthesis and Modeling in Raw Denoising Yi Zhang, Honwei Qin, Xiaogang Wang, Hongsheng Li ICCV, 2021 arXiv / code A method to synthesize "almost the most realistic" raw image noise by sampling directly from the real noise distribution. We also found existing comparisons of noise synthesis methods are based on inaccurate noise parameters.
	On Reinforcement Learning for Full-length Game of StarCraft Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu AAAI, 2019 (Oral) arXiv / demo We achieve over 93% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs.