高峰,2007年獲得英國(guó)倫敦大學(xué)學(xué)院(University College London)計(jì)算機(jī)科學(xué)學(xué)士學(xué)位,本科畢業(yè)設(shè)計(jì)工作是人工智能領(lǐng)域計(jì)算機(jī)人臉表情識(shí)別技術(shù)。2018年獲得3044am永利集團(tuán)計(jì)算機(jī)科學(xué)博士學(xué)位,主要以人工智能前沿技術(shù)研究探索計(jì)算機(jī)科學(xué)與繪畫(huà)藝術(shù)交叉領(lǐng)域。清華大學(xué)未來(lái)實(shí)驗(yàn)室博士后?,F(xiàn)為3044am永利集團(tuán)助理教授。主要研究領(lǐng)域?yàn)橛?jì)算機(jī)與藝術(shù)交叉學(xué)科,探索人類未來(lái)生活中人工智能技術(shù)在安防、醫(yī)療健康、教育、藝術(shù)等領(lǐng)域的應(yīng)用。多篇論文發(fā)表于國(guó)際頂級(jí)多媒體會(huì)議ACM Multimedia、IEEE會(huì)刊、國(guó)內(nèi)核心期刊《計(jì)算機(jī)學(xué)報(bào)》等,相關(guān)研究成果已成功應(yīng)用于中國(guó)美術(shù)家協(xié)會(huì)、雅昌集團(tuán)等單位。
2007年獲得英國(guó)倫敦大學(xué)學(xué)院(University College London)計(jì)算機(jī)科學(xué)學(xué)士學(xué)位,本科畢業(yè)設(shè)計(jì)工作是人工智能領(lǐng)域計(jì)算機(jī)人臉表情識(shí)別技術(shù)。2018年獲得3044am永利集團(tuán)計(jì)算機(jī)科學(xué)博士學(xué)位,主要以人工智能前沿技術(shù)研究探索計(jì)算機(jī)科學(xué)與繪畫(huà)藝術(shù)交叉領(lǐng)域
[5].Feng GAO, Xinfeng Zhang, Yicheng Huang, Yong Luo, Xiaoming Li, Lingyu DUAN, Data-driven lightweight interest point selection for large-scale visual search, IEEE Transactions on Multimedia,20(10),2774-2782,2018
[6].Jie Chen, Ling-Yu Duan, Feng Gao, Jianfei Cai, Alex C. Kot, Tiejun Huang, A Low Complexity Interest Point Detector, IEEE Signal Processing Letters, 22(2):172-176, 2015
[7].Yuwei Wu, Feng Gao, Yicheng Huang, Jie Lin, Vijay Chandrasekhar, Junsong Yuan, and Ling-Yu Duan, Codebook-free Compact Descriptor for Scalable Visual Search, IEEE Transactions on Multimedia, 21(2):388-401,2019
會(huì)議論文:
[8].Feng Gao, Yihang Lou, Yan Bai, Shiqi Wang, Tiejun Huang, Ling-Yu Duan, Improving Object Detection with Region Similarity Learning,2017 IEEE International Conference on Multimedia & Expo, Pages:1488-1493,July 2017,Hong Kong, China
[10].Yan Bai, Feng Gao, Yihang Lou, Shiqi Wang, Tiejun Huang, Ling-Yu Duan, Incorporating Intra-Class Variance to Fine-Grained Visual Recognition,IEEE International Conference on Multimedia & Expo, Pages:1452-1457,July 2017, Hong Kong, China
Qiusi Wang, Feng Gao, Yitong Wang, Ling-Yu Duan, Adaptive Weighted Matching of Deep Convolutional Features for Painting Retrieval,2016 IEEE Second International Conference on Multimedia Big Data (BigMM2016),Pages:194-197,Taipei,China