个人简介

高飞 ，湘湖菁英教授，西安电子科技大学 杭州研究院。分别于2009年和2015年获得西安电子科技大学电子信息工程专业学士学位和信息与通信工程专业博士学位（导师：高新波 教授）。期间，于2012-2013年到澳大利亚悉尼科技大学进行博士联合培养（导师：陶大程 教授）。自2015年至2022年，任职于杭州电子科技大学计算机学院；并于2019年至2020年期间担任浙江省北大信息技术高等研究院双聘研究员。

长期从事计算机视觉和机器学习等方面的研究工作，主要研究兴趣包括计算艺术、智慧医疗、工业视觉等，涉及视觉质量评价、智能内容生成（AIGC）、绘画机器人、医疗影像分析等课题。在IEEE TNNLS、TCyber、TIP、TMM、CVPR、ICCV、IJCAI、IROS等期刊和会议上发表论文30余篇，ESI高被引论文1篇。获得陕西省科学技术奖一等奖、IEEE ICME’21最佳展示奖亚军、全国博士后创新创业大赛铜奖等。主持国家自然科学基金面上项目、青年项目及浙江省自然科学基金青年项目，并参与多项科研课题。目前担任浙江省抗癌协会人工智能肿瘤诊疗专委会委员，国际期刊The Visual Computer (TVCJ)编委，及IEEE TIP, TMM, CVPR, ICCV等国际期刊和会议的审稿人。

• 更多信息： [Github] [Google Scholar] [DBLP] [EN]
• 招生信息 @杭电：
  • 每年预计招收1-3名硕士研究生；
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教学信息

承担的课程 @Github：

• 本科生：《数字图像处理》、《计算机视觉(基础)》、《创新实践》、《人工智能导论》、《机器学习与深度学习》、《深度学习》
• 研究生：《深度学习》

闲言碎语

• 学习的十个好习惯与坏习惯（译）(PDF)
• 研究生复试可能需要注意的事情
• 关于大学生活的一些个人想法(PDF)
• 关于科研的一些个人想法(PNG，参考了个人认同的一些他人的想法)

发表论文

• Fei Gao, Yifan Zhu, Chang Jiang, Nannan Wang, Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation, Proceedings of the International Conference on Computer Vision (ICCV), 7237-7247, 2023. [Github]

• Biao Ma, Fei Gao* , Chang Jiang, Nannan Wang, Gang Xu, “Semantic-aware Generation of Multi-view Portrait Drawings,” the 32nd International Joint Conference on Artificial Intelligence (IJCAI), 1258-1266, 2023. [paper_arxiv] ~ [Github] ~ [project]

• Chang Jiang, Fei Gao, Biao Ma, Yuhao Lin, Nannan Wang, Gang Xu, “Masked and Adaptive Transformer for Exemplar Based Image Translation,” *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 22418-22427. [paper_cvpr] ~ [paper_arxiv] ~ [project]

• Fei Gao, Xingxin Xu, Jun Yu, Meimei Shang, Xiang Li, and Dacheng Tao, “Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation,” IEEE Transactions on Image Processing, vol. 30, pp. 3487 - 3498, 2021. [paper_ieee] ~ [project]

• Jun Yu, Xingxin Xu, Fei Gao, Shengjie Shi, et al. “Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs,” *IEEE Transactions on Cybernatics, vol. 51, no. 9, pp. 4350 - 4362, 2021. (Corresponding Author) [project] ~ [paper_arxiv] ~ [paper_ieee]

• Hanliang Jiang, Fuhao Shen, Fei Gao*, Weidong Han. Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification. Pattern Recognition, 113: 107825, 2021. [paper@PR] ~ [paper@arxiv] ~ [project] (Corresponding Author)

• Fei Gao, Jingjie Zhu, Zeyuan Yu, Peng Li, Tao Wang, “Making Robots Draw A Vivid Portrait In Two Minutes,” in the Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), pp. 9585-9591, Las Vegas, USA, 2020. [paper_iros] ~ [paper_ariv] ~ [project]

  • AiSketcher: Portrait Drawing Robot. (妙绘艺术微信小程序可试用)

• Lin Zhao, Meimei Shang, Fei Gao*, et al. “Representation Learning of Image Composition for Aesthetic Prediction,” Computer Vision and Image Understanding (CVIU), vol. 199, 103024, Oct. 2020. [project]~[paper]

• 黄菲, 高飞, 朱静洁, 戴玲娜, 俞俊. 基于生成对抗网络的异质人脸图像合成: 进展与挑战[J]. 南京信息工程大学学报, 2019, 11(6): 660~681. [paper]

  Fei Huang, Fei Gao, et al. Heterogeneous face synthesis via generative adversarial networks: progresses and challenges. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2019, 11(6): 660-681. (In Chinese)

• Fei Gao, Shengjie Shi, et al., “Improving Facial Attractiveness Prediction via Co-attention Learning,” 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4045-4049, 12-17 May 2019. [paper]~[project]

• 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. [paper]

• Fei Gao, Yi Wang, Panpeng Li, et al., “DeepSim: Deep similarity for image quality assessment,” Neurocomputing, vol. 157, pp. 104-114, 2017. [paper]~[project]

• Fei Gao and Jun Yu, “Biologically inspired image quality assessment,” Signal Processing, vol. 124, pp. 210-219, 2016. [paper] ~ [project]

• 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.

• 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.