Hengyuan Zhao (赵恒远)
From 2022/01, Hengyuan will be a Ph.D. student at NUS' Show Lab, where he will be supervised by Prof. Mike Shou. He formerly worked as a research intern at VIS Baidu Inc. and SenseTime Inc., where he concentrated on image restoration techniques including as super-resolution, denoising, deblurring, and colorization to restore old videos and images. At the same time, he was working as a research assistant supervised by Prof. Chao Dong and Prof. Yu Qiao at the XPixel Group at Shenzhen Institutes of Advanced Technology (SIAT).
06/2021-10/2021: As a research intern, I joined SenseTime Inc.'s MIG and worked with Fan Zhang.
12/2020-06/2021: As a research intern, I joined Baidu Inc.'s Vision Technology (VIS) and worked with Wenhao Wu.
09/2016-06/2020: I was a undergraduate student at Nanjing University of Posts and Telecommunications, Nanjing, China.
- [11/2021] Congratulation!!! I will take part in Show Lab in the January of 2022.
- [06/2021] Join SenseTime, work with Fan Zhang.
- [03/2021] One paper accepted by CVPR, 2021.
- [12/2020] Join VIS, Baidu, worked with Wenhao WU.
- [08/2020] One paper accepted by ECCV Workshops, 2020.
- [05/2020] Participate the Efficient Super-Resoluton Challenge of AIM 2020 (ECCV Workshops). We got fourth place and lowest parameters.
- [09/2019] Join MMLAB at SIAT, supervised by Yu Qiao and Chao Dong.
- [08/2019] One paper accepted by ICCV Workshops, 2019.
Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
Computer Vision and Pattern Recognition (CVPR 2021)
Efficient Image Super-Resolution Using Pixel Attention
European Conference on Computer Vision Workshops (ECCVW 2020)
We got fourth place of Efficient Image Super Resolution Challenge in total 150 participants. (The lowest paramters, 272K)
A Simple and Robust Deep Convolutional Approach to Blind Image Denoising
International Conference on Computer Vision Workshops (ICCVW 2019)
Very Lightweight Photo Retouching Network with Conditional Sequential Modulation