研究生培养

宋巍（教授）

发布者：张程冬发布时间：2024-03-26浏览次数：3253

  

宋巍 Wei SONG (Professor, Ph.D)


姓名 Name：

宋巍 Wei Song

导师类别Supervision：

硕导、博导 Master and Doctoral Supervisor

所在专业Discipline：

软件工程

Software Engineering

研究方向 Research Interests：

计算机视觉 Computer vision

海洋大数据分析 Marine big data analysis

学院/单位 Department：

威尼斯wns8885566 College of Information Technology

邮箱E-Mail：

wsong@shou.edu.cn

通讯地址 Address：

上海市浦东新区沪城环路999号, 201306

No.999 Hu Cheng Huan Road, Pudong New Area, Shanghai 201306

简介 Introduction：

宋巍教授，省部级人才计划获得者，主要从事计算机视觉、海洋大数据分析等方向研究，在水下视觉增强、海冰分类、海浪信息检测预测等领域开展了系统性研究。近年来主持国家自然科学基金项目2项和上海市科委项目2项。出版《Marine Big Data》英文专著1部，在国内外重要期刊和国际会议上发表学术论文80余篇，获得授权发明专利10项，获上海市教学成果二等奖、上海海洋科学技术一等奖、上海市总工会“科创中心建设”竞赛科研项目二等奖，国家产学研合作创新成果奖一等奖等。

Dr. Song, awarded by the Shanghai Talent Program, is mainly engaged in computer vision, ocean big data analysis, and the related applications. She has carried out systematic research in the fields of underwater vision enhancement, sea ice classification, and ocean wave information detection and prediction. In recent years, she has presided over two projects of the National Natural Science Foundation and two projects of the Shanghai Science and Technology Commission. She has published an English monograph on "Marine Big Data", more than 80 academic papers in important journals and international conferences, obtained 10 patents, and won multiple awards, including the Second Prize of Shanghai Teaching Achievement, the First Prize of Shanghai Marine Science and Technology Award, the First Prize of the National Industry-University-Research Cooperation Innovation Achievement Award, etc.

教育经历 Education：

09/2008 – 10/2012 博士(Ph.D)

昆士兰科技大学(Queensland University of Technology, Brisbane, Australia)

09/2005 – 07/2008 工学硕士(Ms.E)

太原理工大学 （Taiyuan University of Technology, Taiyuan, Shanxi Province, China)

工作经历 Working Experience

2016.1- 至今 教授Professor

威尼斯wns8885566 (Shanghai Ocean University)

2012 - 2015.11研究员 Research Fellow

澳大利亚昆士兰科技大学 (Queensland University of Technology)

获奖情况 Awards

  1. 2022年上海市教学成果奖 二等奖 （the second prize of Shanghai Teaching Achievement）

  2. 2021年上海海洋科学技术奖 一等奖 (the first prize of Shanghai Marine Science and Technology Award)

  3. 2020年上海市总工会“奋进新时代创造新奇迹竞赛”科创竞赛项目 二等奖 (the second prize of Science Innovation Competition Project by Shanghai Federation of Trade Unions)

  4. 2018年，上海市浦东新区科学技术奖 二等奖 (the second prize of Science and Technology Award of Shanghai Pudong New Area)

  5. 2017年获得中国产学研合作创新成果 一等奖 (the first prize of National Industry-University-Research Cooperation Innovation Achievement Award)

  6. 指导全国研究生数学建设竞赛获得二等奖2次、三等奖5次

承担的科研项目Scientific research projects undertaken：

  1. 国家“十四五”重点研发计划（National Key R&D Project), No.2021YFC3101601, 2021-2025, 参与（CI）

  2. 国家自然科学基金面上项目 (National Natural Science Foundation of China, NSFC), No.61972240, 2019.1.1-2023.12.31, 主持 (PI)

  3. 国家自然科学基金青年项目 (National Natural Science Foundation of China for Youth), No.41906179, 2018.1.1-2020.12.31，主持 (PI)

  4. 上海市科委部分地方高校能力建设项目 (Program for capacity building of local colleges and universities funded by Shanghai Science and Technology Commission)，No.20050501900, 2020.10.1-2023.9.30，主持 (PI)

  5. 上海市科委部分地方高校能力建设项目(Program for capacity building of local colleges and universities funded by Shanghai Science and Technology Commission)，No.17050501900, 2017.7.1 - 2020.6.30，主持 (PI)

代表性论著 Representative publications：

Book & Book Chapters:

  1. HUANG D, SONG W, ZOU G, Marine Big Data [M]. World Scientific Publishing. 2019, ISDN:9789811202483

  2.  SONG W.,Tjondronegoro, D., and Docherty, M..  User-centered study on quality of mobile video services [M]. In Tjondronegoro, D.  (Eds.), Tools for Mobile Multimedia Programming and Development. IGI Global, 2013.

  3. SONG W., Tjondronegoro, D., and Docherty, M. Understanding user experience of mobile video: Framework, measurement,  and optimization[M]. In Tjondronegoro, D. (Eds.), Mobile Multimedia – User  &Technology Perspectives, InTech Open Access, 2012.

Journals:

  1. XU S, ZHANG M, SONG W*, et al. A systematic review and analysis of deep learning-based underwater object detection[J/OL]. Neurocomputing, 2023, 527: 204-232. DOI:10.1016/j.neucom.2023.01.056.

  2. WANG J, LI X, ZHANG Z, SONG W*, GUO WQ. Ranked Similarity Weighting and Top-nk Sampling in Deep Metric Learning[J/OL]. IEEE Transactions on Multimedia, 2022: 1-10. DOI:10.1109/TMM.2022.3225738.

  3. SONG W, LI H, HE Q, et al. E-MPSPNet: Ice–Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision[J/OL]. Remote Sensing, 2022, 14(22): 5753. DOI:10.3390/rs14225753.

  4. LIU X, SONG W*, HE Q, et al. Speeding Up Subjective Video Quality Assessment via Hybrid Active Learning[J/OL]. IEEE Transactions on Broadcasting, 2022: 1-14. DOI:10.1109/TBC.2022.3210385.

  5. WANG Y, SONG W, TAO W, et al. A systematic review on affective computing: emotion models, databases, and recent advances[J/OL]. Information Fusion, 2022, 83-84: 19-52. DOI:10.1016/j.inffus.2022.03.009.

  6. SONG W, GAO W, HE Q, et al. SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay[J/OL]. Remote Sensing, 2021, 14(1): 168. DOI:10.3390/rs14010168.

  7. SONG W, LI M, GAO W, et al. Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal Features Learning[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(12): 9887-9901. DOI:10.1109/TGRS.2020.3049031.

  8. ZHANG M, XU S, SONG W*, et al. Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion[J/OL]. Remote Sensing, 2021, 13(22): 4706. DOI:10.3390/rs13224706.

  9. DU Y, SONG W*, HE Q, et al. Deep learning with multi-scale feature fusion in remote sensing for automatic oceanic eddy detection[J/OL]. Information Fusion, 2019, 49: 89-99. DOI:10.1016/j.inffus.2018.09.006.

  10. SONG W, WANG Y, HUANG D, et al. Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map[J/OL]. IEEE Transactions on Broadcasting, 2020, 66(1): 153-169. DOI:10.1109/TBC.2019.2960942.

  11. WANG Y, SONG W*, FORTINO G, et al. An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging[J/OL]. IEEE Access, 2019: 1-1. DOI:10.1109/ACCESS.2019.2932130.

Conferences:

  1. WANG Y, SUN Y, SONG W, et al. DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos[C/OL]//Proceedings of the 30th ACM International Conference on Multimedia. Lisboa Portugal: ACM, 2022: 101-110.

  2. WANG J, LI X, SONG W, ZHANG Z, GUO W. Multi-Hierarchy Proxy Structure for Deep Metric Learning[C/OL]//ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore, Singapore: IEEE, 2022: 1645-1649.

  3. SONG W, LI Q, HE Q, ZHOU X, CHEN Y. Determining Wave Height from Nearshore Videos Based on Multi-level Spatiotemporal Feature Fusion[C]//2021 International Joint Conference on Neural Networks (IJCNN). 2021Shenzhen, China: : 1–8.

  4. WANG J, ZHANG Z, HUANG D, Song W, WEI Q, LI X. IEEE, 2021. A Ranked Similarity Loss Function with pair Weighting for Deep Metric Learning[C]//ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021Toronto, Canada: 1760–1764.

  5. SONG W, DAI S, HUANG D, SONG J, ANTONIO L. Median-Pooling Grad-CAM: An Efficient Inference Level Visual Explanation for CNN Networks in Remote Sensing Image Classification[G]//LOKOČ J, SKOPAL T, SCHOEFFMANN K, 等. MultiMedia Modeling. Springer International Publishing, 2021 , 12573: 134–146.

  6. SONG W, WANG Y, HUANG D, TJONDRONEGORO D. A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration[M/OL]//HONG R, CHENG W H, YAMASAKI T, et al. Advances in Multimedia Information Processing – PCM 2018: 11164. Cham: Springer International Publishing, 2018: 678-688