Chunhua Shen
Also known as 沈春华
Chunhua Shen is a computer-vision researcher whose work spans image segmentation and foundation models. He studied mathematics and physics, then speech signal processing, at Nanjing University; he earned a computer-vision PhD at Adelaide and an applied-statistics MPhil at Australian National University. His career took him through NICTA and back to Adelaide in 2011, where he was a computer-science professor by 2018. He held an ARC Future Fellowship from 2012 to 2016 and coauthored RefineNet for high-resolution semantic segmentation. As of October 2026, he is a Chair Professor at Zhejiang University and a Professor at Zhejiang University of Technology, where he develops foundation models for open-set deployment.
- Works
- 653
- Citations
- 70,590
- h-index
- 127
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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1995–2002
Studying at Nanjing University
He studies mathematics and physics for a BSc and speech signal processing for an MSc at Nanjing University from 1995 to 2002.
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2006–2009
Graduate degrees in Australia
He earns a computer-vision PhD at the University of Adelaide in 2006 and an applied-statistics MPhil at Australian National University in 2009.
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2006–2011
Research at NICTA and ANU
He works in NICTA's computer-vision program for about six years and holds an adjunct position at Australian National University from 2006 to 2011.
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2011
Returning to Adelaide
He moves back to the University of Adelaide in 2011.
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2012–2016
An ARC Future Fellowship
He holds an ARC Future Fellowship from 2012 to 2016.
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2017
RefineNet for semantic segmentation
With Guosheng Lin, Anton Milan and Ian Reid, he coauthors RefineNet, a multi-path network for high-resolution semantic segmentation in 2017.
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By 2018
Professor at Adelaide
By 2018, he is a computer-science professor at the University of Adelaide.
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As of October 2026
Foundation models in Zhejiang
As of October 2026, he is a Chair Professor at Zhejiang University and a Professor at Zhejiang University of Technology. His current program develops foundation models for reliable open-set deployment.
Sources and credits
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