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Prof. Xuming He / 何旭明 副教授、研究员

Tel:  (021) 20685378
Office: Room 1A-304D, SIST Building, No.393 Huaxia Middle Road, Pudong Area


  • Computer Vision:
          Scene Understanding / Semantic Segmentation / Object Detection / Vision and Language
  • Machine Learning:
          Graphical Models / Structured Prediction / Weak and semi-supervised learning / Approximate Inference
  • Visual Prosthesis:
          Visual processing for bionic eye / Face analysis and enhancement


      Xuming He received the B.Sc. and M.Sc. degrees in electronics engineering from Shanghai Jiao Tong University, in 1998 and 2001, respectively, and Ph.D. degree in computer science from the University of Toronto (machine learning group) in 2008. He held a postdoctoral position in Prof Alan Yuille’s group at the University of California at Los Angeles (UCLA) from 2008 to 2010. After that, he joined in National ICT Australia (NICTA) as a Researcher in 2010 and has been a Senior Researcher since 2013. He was also an adjunct Research Fellow level B from 2010 to 2012 and level C since 2013 at the Australian National University (ANU). He will join in ShanghaiTech as an associate professor, PI in early 2017.

      His general research interests lie in machine learning, computer vision and biological vision.  In particular, his current research focuses on semantic segmentation, object detection, 3D scene understanding, visual motion analysis, efficient inference and learning in structured models, and artificial vision. He has more than 50 conference and journal publications, including CVPR, ICCV, ECCV, AAAI, NIPS, IEEE TIP, IEEE TPAMI, Journal of Vision, etc.


1. Zeeshan Hayder, Xuming He, Mathieu Salzmann, Structural Kernel Learning for Large Scale Multiclass Object Co-Detection, International Conference on Computer Vision (ICCV), 2015

2. Xuming He, Stephen Gould, An Exemplar-based CRF for Multi-instance Object Segmentation,IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014

3. Shuang Wu, Xuming He, Hongjing Lu, and Alan Yuille, A Unified Model of Short-range and Long-range Motion Perception, Annual Conference on Neural Information Processing Systems (NIPS), 2010

4. Liam Stewart, Xuming He, Richard S. Zemel, Learning Flexible Features for Conditional Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(8), 1415-1426, 2008.

5. Xuming He, Richard Zemel, and Miguel Carreira-Perpinan, Multiscale Conditional Random Fields for Image Labelling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004