Biography

Fei Gao is currently with School of Computer Science and Technology, in Hangzhou Dianzi University (HDU). He received his Bachelor Degree in Electronic Engineering and Ph.D. Degree in Information and Communication Engineering from Xidian University (Xi'an, China) in 2009 and 2015, respectively. His Ph.D. advisor is Prof. Xinbo Gao. From Oct. 2012 to Sep. 2013, he was a Visiting Ph.D. Candidate in Prof. Dacheng Tao's Group, in University of Technology, Sydney (UTS) in Australia.

He mainly applies machine learning techniques to computer vision problems. His research interests include image quality assessment and enhancement, biomedical image processing, generative models, deep learning, etc. His research results have expounded in 20 publications at prestigious journals and conferences, such as IEEE T-NNLS, Pattern Recognition, Neurocomputing, and Signal Processing. He serverd for a number of journals and conferences, including IEEE Trans. on Image Processing (TIP), IEEE Trans. on Cybernetics (TC), IEEE Trans. on Multimedia (TMM), IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), Information Sciences, Signal Processing, Neurocomputing, and CVPR, etc.

Projects


Boimetric Quality Assessment for Facial Images in-the-Wild
supported by the China National Natural Science Foundation (Grant No. 61601158), Jan. 2017 - Dec. 2019, PI: Fei Gao.
Structured Visual Quality Assessment of Large-Scale Heterogeneous Images
supported by the Natural Science Foundation of Zhejiang Province (Grant No. LQ16F030004), Jan. 2016 - Dec. 2018, PI: Fei Gao. 

Selected Publications


Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Jun Yu, Fei Gao*, Shengjie Shi, et al., "Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs ," Arxiv Preprint, Arxiv:1712.00899. (Corresponding Author) [github]
Blind Image Quality Prediction by Exploiting Multi-level Deep Representations
Fei Gao, Jun Yu, et al., "Blind Image Quality Prediction by Exploiting Multi-level Deep Representations," Pattern Recognition, vol 81, pp. 432-442, Sep. 2018.
Biologically inspired image quality assessment
Fei Gao and Jun Yu, "Biologically inspired image quality assessment," Signal Processing, vol. 124, pp. 210-219, 2016. (ESI Highly Cited Papers)