Xuming He
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Senior Research Scientist
Computer Vision Research Group, Data61, CSIRO
Honorary Senior Lecturer
College of Electrical & Computer Science, ANU
Address
7 London Circuit, Tower A, Level 1 (L1-23)
Canberra, ACT 2601, Australia
Contact
Phone: +61 2 62183782
Email: fname dot lname at anu
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I have joined in the School of Information Science and Technology at ShanghaiTech as an Associate Professor in Jan 2017. My new webpage is here.
News
[11/2016] Congratulations to Buyu Liu and Tao Wang for successfully completing their PhDs.
[08/2016] Two papers on object segmentation/representation accepted in ACCV 16.
[07/2016] Two papers on scene parsing accepted in ECCV 16.
[03/2016] One paper on object proposal generation accepted in CVPR 16.
Research Interest
Image parsing and semantic segmentation
3D object and scene representations
Learning and inference in undirected graphical models
Understanding human vision system
Image, language and knowledge representation
Selected Publications
Buyu Liu, Xuming He,
Multiclass Semantic Video Segmentation With Object-Level Active Inference,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
[pdf] [suppl zip]
Xuming He, Stephen Gould,
An Exemplar-based CRF for Multi-instance Object Segmentation,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
[pdf]
Yansheng Ming, Hongdong Li, Xuming He,
Connected Contours: a Contour Completion Model That Respects Closure-Effect,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
[pdf]
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, Vancouver, Canada
[pdf]
Xuming He, and Richard S. Zemel,
Learning Hybrid Models for Image Annotation with Partially Labeled Data,
Annual Conference on Neural Information Processing Systems (NIPS), 2008
[pdf]
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
[pdf]
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