高飞 副教授 硕士生导师
计算机科学与技术
计算机视觉，机器学习，人工智能+艺术，人工智能+医学

gaofei@hdu.edu.cn



教授简介
发表论文
高飞，于2015年于西安电子科技大学获得信息与通信工程专业博士学位，于2012年至2013年到澳大利亚悉尼科技大学访问学习。自2015年7月起，任职于杭州电子科技大学计算机学院。主要研究兴趣为计算机视觉与机器学习，涉及智能视觉艺术、医学影像分析、智能机器人等课题。个人主页：aiart.live
研究领域：主要研究兴趣为计算机视觉与机器学习，包括视觉质量评价与增强、图像生成及风格迁移、医疗影像智能分析、深度学习等课题。现已在主流国际期刊、会议上发表论文20余篇，主持和参与多项国家自然科学基金、浙江省自然科学基金项目。以主要参与人员身份获得2016年陕西省科学技术奖一等奖及2011年陕西高等学校科学技术一等奖。还担任多个国际顶级期刊和会议的审稿人。

1.Jun Yu, Xingxin Xu, Fei Gao*, et al., “Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs ,” Arxiv Preprint, Arxiv:1712.00899. (Corresponding Author)
2.H. Jiang, Fei Gao*, Xingxin Xu, et al., “Attentive and Ensemble 3D Dual Path Networks for Pulmonary Nodules Classification,” Neurocomputing, 2019. (Accepted) (Corresponding Author)
3.Fei Gao, Ziyun Li, et al., “Style-adaptive Photo Aesthetic Rating via Convolutional Neural Networks and Multi-task Learning,” Neurocomputing, 2019. (Accepted)
4.Fei Gao, Jun Yu, Suguo Zhu, Qingming Huang, Qi Tian, “Blind Image Quality Prediction by Exploiting Multi-level Deep Representations,” Pattern Recognition, vol 81, pp. 432-442, Sep. 2018.
5.Jun Yu, Kejia Sun, Fei Gao *, Suguo Zhu, “Face biometric quality assessment via light CNN,” Pattern Recognition Letters, vol. 107, pp. 25-32, 1 May 2018. (Corresponding Author)
6.J. Yu, X. Yang, Fei Gao, et al., “Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking,” in IEEE Transactions on Cybernetics, vol. 47, no. 12, pp. 4014 - 4024, Dec. 2017
7.Fei Gao, Yi Wang, Panpeng Li, et al., “DeepSim: Deep similarity for image quality assessment,” Neurocomputing, vol. 157, pp. 104-114, 2017.
8.Fei Gao and Jun Yu, “Biologically inspired image quality assessment,” Signal Processing, vol. 124, pp. 210-219, 2016. (ESI Highly Cited Papers)
9.Fei Gao, Dacheng Tao, Xinbo Gao, and Xuelong Li, “Learning to rank for blind image quality assessment,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2275-2290, Oct. 2015.
10.Xinbo Gao, Fei Gao, Dacheng Tao, and Xuelong Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 12, pp. 2013-2026, 2013.