Computer Vision Group,
    School of Electronic Engineering and Computer Science,
    Queen Mary University of London,
    London E1 4NS,
    United Kingdom.

    Email: q.dong AT qmul.ac.uk [Linkedin] [Google Scholar]

QI DONG is a PhD student (Near graduation) at the Computer Vision Group of Queen Mary University of London (QMUL), London, UK. Her adviser is Prof. Shaogang Gong and she also works closely with Dr. Xiatian Zhu. Her research interests include Computer Vision and Machine Learning, particularly in Deep learning and Attribute analysis.
Prior to joining QMUL, she received the M.Eng. and B.Eng. degrees in Computer Science from Sichuan University and Tianjin University of Technology, respectively.


Jiabo Huang, Qi Dong, Xiatian Zhu, Shaogang Gong. Unsupervised Deep Learning by Neighbourhood Discovery. In Proc. Thirty-sixth International Conference on Machine Learning, Long beach, CA, USA, June 2019. (ICML2019) .   [PDF][Code][Project Page]


Qi Dong, Xiatian Zhu, Shaogang Gong. Single-Label Multi-Class Image Classification by Deep Logistic Regression. In Proc. AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, January 2019. (AAAI2019) . Oral Presentation.   [PDF] [Slides] [Poster] [Code]


Qi Dong, Shaogang Gong, Xiatian Zhu. Imbalanced Deep Learning by Minority Class Incremental Rectification. IEEE Transactions on Pattern Analysis and Machine Intelligence, accepted, 2018. (TPAMI).   [PDF][Project page]


Qi Dong, Shaogang Gong, Xiatian Zhu. Class Rectification Hard Mining for Imbalanced Deep Learning. In Proc. International Conference on Computer Vision, Venice, Italy, Oct 2017 (ICCV2017).   [PDF]


Qi Dong, Shaogang Gong, Xiatian Zhu. Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes. In Proc. IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, CA, USA, March 2017 (WACV2017).   [PDF]


Qi Dong, Yanli Liu, Qijun Zhao, Hongyu Yang. Detecting soft shadows in a single outdoor image: From local edge-based models to global constraints. In Computers & Graphics, Volume 38, February 2014, Pages 310-319. (CG)   [PDF]  


  • Computer vision and deep learning (Spring 2019)
  • Data mining and Machine learning (Fall 2016)

Updated Dec 2016
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