Chaofeng Chen
University of Hong Kong
27 Papers
30 Citations
Chaofeng Chen is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Computer science & Face (geometry). The author has an hindex of 7, co-authored 16 publications. Previous affiliations of Chaofeng Chen include Alibaba Group.
Chat about Author
Papers
STAR-Net: a SpaTial attention residue network for scene text recognition
Wei Liu,Chaofeng Chen,Kwan-Yee K. Wong,Zhizhong Su,Junyu Han +4 more
- 01 Jan 2016
TL;DR: This paper presents a novel SpaTial Attention Residue Network (STAR-Net) for recognising scene texts and emphasises the importance of representative image-based feature extraction from text regions by the spatial attention mechanism and the residue learning strategy.
•Proceedings Article
Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition
Wei Liu,Chaofeng Chen,Kwan-Yee K. Wong +2 more
- 27 Apr 2018
TL;DR: A novel hierarchical attention mechanism (HAM) which consists of a recurrent RoIWarp layer and a characterlevel attention layer which can handle different types of distortion that are hard, if not impossible, to be modelled by a single global transformation.
221
Learning Spatial Attention for Face Super-Resolution
TL;DR: SPARNet as mentioned in this paper introduces a spatial attention mechanism to the vanilla residual blocks to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions.
148
•Proceedings Article
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Xiaoming Li,Chaofeng Chen,Chaofeng Chen,Shangchen Zhou,Xianhui Lin,Wangmeng Zuo,Lei Zhang +6 more
- 01 Aug 2020
TL;DR: Zhang et al. as mentioned in this paper proposed a deep face dictionary network (DFDNet) to guide the restoration process of degraded observations, which used K-means to generate deep dictionaries for perceptually significant face components (left/right eyes, nose and mouth) from high-quality images.
146
Learning Spatial Attention for Face Super-Resolution
TL;DR: A novel SPatial Attention Residual Network (SPARNet) built on the authors' newly proposed Face Attention Units (FAUs) for face super-resolution is introduced, and a spatial attention mechanism to the vanilla residual blocks is introduced to enable the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions.
120