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Gui-Song Xia is currently a Professor of Computer Science at Wuhan University. He received his B.Sc. and M.Sc. degree from Wuhan University in 2005 and 2007, respectively, and got a Ph.D. degree with speciality in Signal and Images from Telecom ParisTech in March 2011. Since April 2011, he had worked as a postdoctoral researcher at CNRS ( LTCI and CEREMADE) for about 1.5 years. In December 2012, he joined Wuhan University.

His current research concentrates on mathematical modeling of images, computer vision, pattern recognition and their applications in remote sensing imaging. On those topics, he has co-/authored more than 110 articles on international journals/conferences, such as International Journal of Computer Vision (IJCV), IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Trans. on Image Processing (TIP), SIAM Journal on Imaging Science (SIIMS), Pattern Recognition (PR), IEEE Trans. on Geoscience and Remote Sensing (TGRS), CVPR, ECCV, BMVC, ICIP and ICPR. He serves currently as Associate Editors of Pattern Recognition, Signal Processing: Image Communications and EURASIP Journal on Image and Video Processing, and Guest Editor of IEEE Trans. on Big Data , Pattern Recognition Letter, Geo-Spatial Information Science, etc. He is a senior member of IEEE.

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News


Educations

  • 2007.10-2011.03: Ph.D. in Signal and Images, Telecom ParisTech, Paris, France.
  • 2005.10-2007.06: M.Sc. in Signal and Images Processing, Wuhan University, Wuhan, China.
  • 2001.09-2005.06: B.Sc. in Electronic Engineering, Wuhan University, China.

Working Experiences


Research Interests

  • Mathematical modeling of images/videos
  • Machine learning in vision problems
  • Computational and photogrammetric vision
  • Data mining of big remote sensing images
  • Scene cognition based on UAV platform

Recently Representative Work

  • Learning to Extract Building Footprints from Off-Nadir Aerial Images, [PDF]. with J. Wang et. al..
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), in press.
  • Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges, [arXiv, Dataset, Code, Model]. with J. Ding et. al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), in press.
  • Unsupervised Pretraining for Object Detection by Patch Reidentification, [arXiv]. with J. Ding et. al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), in press.
  • Unmixing Convolutional Features for Crisp Edge Detection, [arXiv, Codes]. with L. Huan et. al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), in press.
  • Learning Regional Attraction for Line Segment Detection, [PDF, Page]. with N. Xue et. al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 43, No.6, pp.1998-2013, 2021.
  • Gliding vertex on HBB for multi-oriented object detection, [arXiv, codes]. with Y. Xu et. al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol.43, No.4, pp.1452-1459, 2021.
  • A Functional Representation for Graph Matching [Project Page]. with F.-D. Wang et. al.
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol.42, No.11, pp.2737-2754, 2020.
  • Land-Cover Classification with HR-RS Images using Transferable Deep Models [arXiv, Page]. with X.-Y. Tong et. al.
    Remote Sensing of Environment (RSE), 2020.
  • Holistically-Attracted Wireframe Parsing [arXiv]. with N. Xue et. al.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  • Learning RoI Transformer for Detecting Oriented Objects in Aerial Images [PDF], with J. Ding et. al.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2019.
  • DOTA: A Large-scale dataset for object detection in aerial images [PDF, Project Page, gitHub]. with X. Bai et. al.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  • Anisotropic-scale junction detection and matching for indoor images . [PDF], with N. Xue et. al.
    IEEE Trans. on Image Processing (TIP), Vol.27, No.1, pp.78-91, 2018.
  • Texture characterization using shape co-occurrence patterns. [PDF], with G. Liu et. al.
    IEEE Trans. on Image Processing (TIP), Vol.26, No. 10, pp.5005 - 5018, 2017.
  • AID: A benchmark dataset for performance evaluation of aerial scene classification . [PDF, project-page], with J. Hu et. al.
    IEEE Trans. on Geoscience and Remote Sensing (TGRS), Vol. 55, No.7, pp.3965 - 3981, 2017.