Li Liu received the Ph.D. degree in information and communication
engineering from the National University of Defense Technology (NUDT),
China, in 2012. From 2008 to 2010, she worked as a Visiting Student at the University of Waterloo,
Canada, advised by Professor Paul Fieguth . From 2015 to 2016,
she spent ten months visiting the Multimedia Laboratory at the Chinese University of Hong Kong,
working with Professor Xiaogang Wang . From 2016.12 to 2018.9, she worked as a senior researcher at CMVS of the University of Oulu,
Finland, working with Professor Matti Pietikainen.
She was a cochair of nine International Workshops at CVPR, ICCV, and ECCV.
She served as guest editor for two special issues in IEEE TPAMI and one in IJCV.
Her current research interests include computer vision, pattern recognition and machine learning.
Her papers have currently over 5380 citations in Google Scholar (Until 2021.12.31).
Her recent survey paper titled Deep Learning for Generic Object Detection:
A Survey published in IJCV in 2020 now has 1170 citations (Until 2021.12.31) according to Google Scholar.

Recent Events

· News! 2022/04/05 Three papers accepted by CVPR 2022. One paper accepted by IJCV.

· News! 2021/12/30 Joining the IEEE TCSVT Editorial Board!

· News! 2021/11 Two papers were accepted by BMVC 2021! Congratulations to Wuti and Jiehua!

· News! 2021/10 A number of papers accepted by IEEE TIP, IEEE TMM, IEEE TGRS, IEEE TCYB!

· News! 2021/8 Two papers were accepted by ACM MM 2021! Congratulations to Wanxia and Changchong!

· News! 2021/7 Three papers were accepted by ICCV 2021, including one ICCV oral paper! Congratulations to Zhuo etc.!

· News! 2021/7 One paper was accepted by ICML 2021!

· News! 2021/2/10 Joining Pattern Recognition as AE

· News! 2020/9/1 (submission deadline over) IEEE TPAMI Special Issue on Learning with Fewer Labels in Computer Vision

· News! 2020/8/1 Two papers were accepted by ECCV 2020! Congratulations to Zhuo Su and Linpu Fang!

Research Hightlights

Project: LESLearn:

Label, Energy and Sample Efficient Feature Representation and Learning in Computer Vision

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