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College of Engineering and Computer Science

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Dr. Guo-Jun Qi 

Assistant Professor

Department of Computer Science 

Director of MAPLE research lab

 

Email:
guojun.qi at ucf dot edu | guojunq at gmail dot com
Phone: (407) 823-2764
FAX: (407) 823-5835

 

Address:
University of Central Florida
Department of Computer Science
4328 Scorpius HEC 318
Orlando, FL 32816

Laboratory: 

Machine Perception and Learning (MAPLE)


Research Interests

  • Machine Learning and Pattern Recognition 
  • Computer Vision and Multimedia Computing 
  • Data Mining and Data Analytics - Knowledge Discovery and Representation


Awards

  • 2015 Best Paper Runner-up, International ACM Conference on Multimedia 
  • 2014 Best Student Paper Award (co-recipent as the mentor of the student author), IEEE International Conference on Data Mining (ICDM).
  • 2013 "Best of ICDE Paper" by IEEE Transactions on Knowledge and Data Engineering
  • 2011,2012 IBM Fellowship, IBM
  • Best Paper Award, The 15th ACM International Conference on Multimedia (ACM SIGMM) 
  • 2007 Best Research Intern, Microsoft Research Asia 
  • 2007 Microsoft Fellowship, Microsoft 
  • 2005 Guo Moruo Scholarship, USTC (Top Scholarship in USTC)

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News

  • http://www.wallpapersxl.com/wallpapers/1061x943/new-star/16998/new-star-new-icon-169984.jpg We propose a Loss-Sensitive GAN (LS-GAN), along with a generalized version (GLS-GAN) unifying both WGAN and LS-GAN. We prove both consistency and generalizability of the LS-GAN. See more details in [pdf] and the Appendix D. Moreover, you might be interested in taking a look at an incomplete map of GANs in our view [url]. 
  • The  recent version uses a projection onto the manifold of real data to calculate the margin between fake and real samples. Thus, the vanishing projection magin would be a better indicator of how well fake samples are generated compared with their real sample counterparts.  Details can be found at [pdf]. 

Recent News:

  • http://www.wallpapersxl.com/wallpapers/1061x943/new-star/16998/new-star-new-icon-169984.jpgWe have a paper "Interleaved Group Group Convolutions for Deep Neural Networks" accepted by ICCV 2017, where a super compact and fast deep convolutional model was develop that can be deployed on mobile devices. Two types of group convolutions, a primal group sparse convolution and a dual point-wise permutation convolution, are developed to make the model more efficient. [pdf]
  • http://www.wallpapersxl.com/wallpapers/1061x943/new-star/16998/new-star-new-icon-169984.jpgWe released our sources for our ICML 2017 and KDD 2017 papers on State-Frequency LSTM [githubpdf] and stock price prediction [githubpdf].
  • Congratulations to Hao and Liheng on their ICML2017 and KDD2017 papers being accepted.  
  • Congratulations to Mr. Joey Velez-Ginorio, an undergraduate researcher of our group, on being selected as a Barry Goldwater scholar. This is the most prestigious undergraduate scholarship across the country established by the United States Congress in honor of United States Senator and former presidential candidate Barry Goldwater to support highly qualified college students to pursue careers in STEM.
  • Dr. Qi will serve as an Area Chair for ICCV 2017.
  • Dr. Qi is serving as an Area Chair for ICME 2017
  • A paper on learning compact features that encode dynamics of video and sensor data has been accepted by ACM TOMM.  
  • A paper on jointly learning label classification and tag recommendation has been accepted by AAAI 2017.  
  • One paper developing an efficient ranking-based hashing algorithm has been accepted for the publication in IEEE Transactions on Pattern Analysis and Machine Intelligence. [pdf] [code
  • One paper "Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement" has been accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • One paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence for classifying images of rarely seen or unseen classes with the help of text labels. [pdf][code]   

Archived News:
  • Two research papers, including an oral presentation "Hierarchically Gated Deep Networks for Segmantic Segmentation", have been accepted for presentation at CVPR 2016, Las Vegas, Nevada. [pdf]
  • One paper has been accepted for plenary presentation at SIGKDD 2016. A fast detection method for brain disorder based on fMRI was presented. It is one order of magnitude faster than state-of-the-art methods with even better accuracy.   
  • Dr. Qi is serving as an Area Chair for ACM Multimedia 2016.   
  • Dr. Qi will serve as a Senior Program Committee Member for KDD 2016.  
  • International Conference on MultiMedia Modeling will go to Miami FL, 4-6 January 2016 [link].   Dr. Qi will serve as program co-chair.  
  • CFP: Special Issue on "Big Media Data: Understanding, Search, and Mining", in IEEE Transactions on Big Data [pdf] (deadline: July 1, 2015). 
  • CFP: "Deep Learning for Multimedia Computing", in IEEE Transactions on Multimedia [pdf] (The new deadline is April 20, 2015).
  • Our full research paper "Weekly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation" has been selected as one of the four best paper candidates to be presented at ACM MM 2015.
  • One paper is accepted by ICCV 2015.  We developed a novel deep LSTM  model for analyzing human actions, where we explore the differential structure over memory states to study the dynamic saliency.
  • One full research paper "Weekly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation" is accepted by ACM MM 2015.  We developed a novel cross-modal label transfer deep network, showing competitive performance on predicting image labels derived from the alignment with text documents.  
  • Dr. Qi is serving as an Area Chair for ACM Multimedia 2015
  • Three papers are accepted by KDD 2015. Congratulations to Vivek, Rohit, Shiyu and Wei!  In these papers, (1) we developed deep networks to reveal the brain neural connectivity by aligning  time-series activiations by neuron fires that are marked by calcium influx;  (2) we invented a new paradigm of dynamic model to select and predict sensors and their readings over time, as compared with the conventional static strategy; and (3) we developed heterogeneous networks to predict the cross-modal relevance between multimodal data.
  • One paper "Temporal-Order Preserving Dynamic Quantization for Human Action Recognition from Multimodal Sensor Streams" accepted by ICMR 2015.  On UTKinect-Action dataset, our best approach has achieved 100% accuracy. Congralulations to Jun and Kai! 
  • One paper "Sparse Composite Quantization" has been accepted by CVPR 2015. Congralutions to Ting!


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