|Xuming He, Associate Professor|
Tel: (021) 20685378
Computer Vision: Scene understanding, Semantic segmentation, Object detection, Vision and language
Machine Learning: Graphical models, Neural networks, Weakly-supervised and meta-learning, Approximate inference
Xuming He is currently the VDI Center Director and the Degree Committee Director.
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 joined ShanghaiTech as an Associate Professor, PI in January, 2017.
Yongfei Liu*, Bo Wan*, Xiaodan Zhu, Xuming He, “Learning Cross-Modal Context Graph for Visual Grounding”, in Proceedings of AAAI Conference on Artificial Intelligence (AAAI), USA, 2020.
Songyang Zhang, Shipeng Yan, Xuming He, “LatentGNN: Learning Efficient Non-local Relations for Visual Recognition”, In Proceedings of International Conference on Machine Learning (ICML),2019
Shipeng Yan, Songyang Zhang, Xuming He, “A Dual Attention Network with Semantic Embedding for Few-shot Learning”, in Proceedings of AAAI Conference on Artificial Intelligence (AAAI), USA, 2019.
Alexander Mathews, Lexing Xie, Xuming He, “SemStyle: Learning to Generate Stylised Image Captions using Unaligned Text”, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Xuming He, Stephen Gould, “An Exemplar-based CRF for Multi-instance Object Segmentation”, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, 2014.
Xuming He, Richard S. Zemel, “Learning Hybrid Models for Image Annotation with Partially Labeled Data”, in Proceedings of Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, 2009.
Xuming He, Richard Zemel, Miguel Carreira-Perpinan, “Multiscale Conditional Random Fields for Image Labeling”, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington DC, USA, 2004.