Greg MoriProfessor and Director
School of Computing Science
Simon Fraser University
8888 University Drive
CANADA V5A 1S6
Faculty office: TASC1 8007
Director's office: ASB 9971
Phone: (778) 782-7111
Fax: (778) 782-3045
Ph.D. in Computer Science, University of California at Berkeley, 2004.
Hon. B.Sc. in Computer Science and Mathematics, University of Toronto, 1999.
Dr. Greg Mori was born in Vancouver and grew up in Richmond, BC. He
received the Ph.D. degree in Computer Science from the University of California,
Berkeley in 2004. He received an Hon. B.Sc. in Computer Science
and Mathematics with High Distinction from the University of Toronto in 1999.
He spent one year (1997-1998) as an intern at Advanced Telecommunications Research
(ATR) in Kyoto, Japan. After graduating from Berkeley, he
returned home to Vancouver and is currently a Professor in
the School of Computing Science at Simon Fraser University.
He was a Visiting Scientist at Google in Mountain View, California in 2014-2015. Returning to Vancouver again, he became Director of the School of Computing Science in May 2015.
Dr. Mori conducts research in computer vision and machine learning, and teaches classes in data structures and programming, artificial intelligence, computer vision, and machine learning. He serves on the editorial boards of IJCV and T-PAMI, the top journals in computer vision, and on the organizing committees for CVPR, ICCV, and ECCV, the top conferences in computer vision. He is privileged to have worked with many excellent students while at SFU.
My research is in computer vision, and is concerned with developing
algorithms that automatically interpret images and videos,
particularly those containing people. I have made significant
contributions towards solving the problems of human pose estimation
and human action recognition. At a broad level, the methodology
followed is to construct features and representations that capture our
intuition regarding these vision problems. We operationalize these via
machine learning algorithms, adapting them to suit our
Specific examples of features and representations include work on superpixels for representing images, motion features for human action recognition, and our structured models for video sequences and group activities. We have developed variants of machine learning algorithms such as hidden Conditional Random Fields (hCRF), Latent Dirichlet Allocation (LDA), latent SVMs, and deep networks to implement these ideas.
Research interests keywords:
|Nazanin Mehrasa successfully defended her M.Sc. thesis Learning Person Trajectory Representations for Team Activity Analysis. Congratulations Nazanin!|
|Karoon Rashedi Nia successfully defended his M.Sc. thesis Automatic Building Damage Assessment Using Deep Learning and Ground-Level Image Data. Congratulations Karoon!|
|Yatao Zhong successfully defended his M.Sc. thesis Learning Person Trajectory Features for Sports Video Analysis. Congratulations Yatao!|
|Mehran Khodabandeh (PhD), Srikanth Muralidharan (PhD), and Nazanin Mehrasa (MSc) had a paper accepted to IAPR International Conference on Pattern Recognition (ICPR), 2016. An unsupervised method for learning features for video segmentation is presented.|
|Srikanth Muralidharan successfully defended his M.Sc. thesis A Hierarchical Deep Temporal Model for Group Activity Recognition. Congratulations Srikanth!|
|Zhiwei Deng (PhD), Arash Vahdat (URA), and Hexiang Hu (BSc) had a paper accepted to IEEE Computer Vision and Pattern Recognition (CVPR), 2016. Structure learning for a message passing deep network is developed and applied to group activity recognition.|
|Hexiang Hu (BSc) and Zhiwei Deng (PhD) had a paper accepted to IEEE Computer Vision and Pattern Recognition (CVPR), 2016. Images are tagged with labels from a hierarchy via a deep learning algorithm that reasons about label relations.|
|Moustafa S. Ibrahim (PhD), Srikanth Muralidharan (MSc), Zhiwei Deng (PhD), and Arash Vahdat (URA) had a paper accepted to IEEE Computer Vision and Pattern Recognition (CVPR), 2016. A multi-level temporal deep network for recognizing group activities in videos is developed.|
|Lei Chen successfully defended his M.Sc. thesis Learning Action Primitives for Multi-Level Video Event Understanding. Congratulations Lei!|
|Zhiwei Deng successfully defended his M.Sc. thesis Deep Structured Models for Group Activity Recognition. Congratulations Zhiwei!|
|Mengyao Zhai successfully defended her M.Sc. thesis Object Detection in Surveillance Video from Dense Trajectories. Congratulations Mengyao!|
|Guang-Tong Zhou successfully defended his Ph.D. thesis Toward Scene Recognition by Discovering Semantic Structures and Parts. Congratulations Guang-Tong!|
|Hossein Hajimirsadeghi successfully defended his Ph.D. thesis Multiple Instance Learning for Visual Recognition: Learning Latent Probabilistic Models. Congratulations Hossein!|
|Hossein Hajimirsadeghi (PhD) had a paper accepted to IEEE International Conference on Computer Vision (ICCV), 2015. The paper presents a boosting method for combining multiple high-capacity models in a structured prediction setting, demonstrating results on a variety of applications including group activity recognition and 3d action recognition.|
|Zhiwei Deng (MSc), Mengyao Zhai (MSc), Lei Chen (MSc), Yuhao Liu (BSc), and Srikanth Muralidharan (MSc) had a paper accepted to the British Machine Vision Conference (BMVC), 2015. A deep learning framework for group activity recognition via message passing is presented.|
|Wang Yan (PDF) and Jordan Yap (BSc) had a paper accepted to the British Machine Vision Conference (BMVC), 2015. Video retrieval in a "one-shot" (single example) setting is improved by leveraging multi-task learning from existing models.|
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