Computer Vision Researcher Media Analytics Department, NEC Laboratories America, Inc. 2033 Gateway Place Suite 200, San Jose, CA 95110

About Me

Hi, I am a Computer Vision Researcher at the Media Analytics Department of NEC Laboratories America, Inc. in San Jose, CA. Before that, I received my PhD degree from the Australian Centre for Visual Technologies, University of Adelaide, Australia under the supervision of Assoc. Prof. Tat-Jun Chin and Prof. David Suter in December 2014, and obtained my BEng (Hons) degree from Vietnam National University - Ho Chi Minh City University of Technology, Vietnam in April 2010.

Generally, I am interested in Computer Vision and Machine Learning. My current researches focus on: SFM/SLAM, 3D Object Localization, and 3D Scene Understanding, with applications in Advanced Driver-Assistance System, Autonomous Driving, and Dashcam Video Analysis.

Curriculum Vitae - LinkedIn

News

• [Jun 2019] Reviewer for 3DRW 2019.
• [Jun 2019] Paper accepted to IROS 2019.
• [May 2019] 4 patents granted by US PTO.
• [Apr 2019] Reviewer for VOCVALC 2019.
• [Mar 2019] Paper accepted to CVPR 2019 as Oral presentation.
• [Sep 2018] Paper accepted to BayLearn 2018.
• [Jul 2018] Paper accepted to TPAMI 2018.
• [Jul 2018] Paper accepted to ECCV 2018.
• [Jun 2018] Reviewer for 3DRW 2018.
• [Feb 2018] Reviewer for VOCVALC 2018.

Publications

GoogleScholar - ResearchGate

	1. *NEW* Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM B. Zhuang*, Q.-H. Tran, G. H. Lee, L. F. Cheong, and M. Chandraker IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 Link - PDF - Supp Project page
	2. *NEW* Learning Structure-And-Motion-Aware Rolling Shutter Correction [Oral Presentation] B. Zhuang*, Q.-H. Tran, P. Ji, L. F. Cheong, and M. Chandraker IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Link - PDF - Supp - Poster - Slides Project page
	3. Deep Supervision with Intermediate Concepts C. Li*, M. Z. Zia, Q.-H. Tran, X. Yu, G. D. Hager, and M. Chandraker IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018 Link - PDF Project page 4. Deep Supervision with Intermediate Concepts C. Li*, M. Z. Zia, Q.-H. Tran, X. Yu, G. D. Hager, and M. Chandraker Bay Area Machine Learning Symposium (BayLearn), 2018 Link - PDF - Poster
	5. Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences M. E. Fathy*, Q.-H. Tran, M. Z. Zia, P. Vernaza, and M. Chandraker European Conference on Computer Vision (ECCV), 2018 Link - PDF - Supp - Poster Project page
	6. Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing C. Li*, M. Z. Zia, Q.-H. Tran, X. Yu, G. D. Hager, and M. Chandraker IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 Link - PDF - Supp - Poster Rendered images (Car) - Rendered images (Chair+Sofa) - Annotated CAD models - Annotated KITTI images
	7. A Continuous Occlusion Model for Road Scene Understanding V. Dhiman*, Q.-H. Tran, J. J. Corso, and M. Chandraker IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Link - PDF - Supp - Poster
	8. Robust Parameter Estimation in Computer Vision: Geometric Fitting and Deformable Registration Q.-H. Tran PhD Thesis, Australian Centre for Visual Technologies, University of Adelaide, 2014 Link - PDF
	9. As-Projective-As-Possible Image Stitching with Moving DLT J. Zaragoza, T.-J. Chin, Q.-H. Tran, M. S. Brown, and D. Suter IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014 Link - PDF - Supp Code - Dataset 1 - Dataset 2 - Dataset 3
	10. Sampling Minimal Subsets with Large Spans for Robust Estimation Q.-H. Tran, T.-J. Chin, W. Chojnacki, and D. Suter International Journal of Computer Vision (IJCV), 2014 Link - PDF
	11. In Defence of RANSAC for Outlier Rejection in Deformable Registration Q.-H. Tran, T.-J. Chin, G. Carneiro, M. S. Brown, and D. Suter European Conference on Computer Vision (ECCV), 2012 Link - PDF - Supp Code

Patents

1. *NEW* Pseudo RGB-D: Exploiting Learned Depth for Robust SfM (provisional application # US/62/892,018, filed on Aug 2019)
2. *NEW* Robust SfM Learner Using Convolutional LSTMs, Memory, and Incremental Learning (provisional application # US/62/884,328, filed on Aug 2019)
3. *NEW* Image/Video Deblurring Using Convolutional Neural Networks with Applications to SFM/SLAM with Blurred Images/Videos (provisional application # US/62/851,259, filed on May 2019)
4. *NEW* Multi-Task Perception Network with Applications to Scene Understanding and Advanced Driver-Assistance System (provisional application # US/62/814,886, filed on Mar 2019)
5. *NEW* Camera Self-Calibration Using Convolutional Neural Networks with Applications to SFM/SLAM with Uncalibrated Images/Videos (provisional application # US/62/793,948, filed on Jan 2019)
6. Rolling Shutter Rectification Using Convolutional Neural Networks with Applications to SFM/SLAM with Rolling Shutter Images/Videos (application # US/16/593,247, filed on Oct 2019)
7. Dense Three-Dimensional Correspondence Estimation with Multi-Level Metric Learning and Hierarchical Matching (application # US/16/526,306, filed on July 2019)
8. Learning Good Features for Visual Odometry (application # US/16/100,445, filed on Aug 2018)
9. Autonomous Vehicle Utilizing Pose Estimation (application # US/16/100,462, filed on Aug 2018)
10. Aerial Drone Utilizing Pose Estimation (application # US/16/100,479, filed on Aug 2018)
11. Dense Correspondence Estimation with Multi-Level Metric Learning and Hierarchical Matching (application # US/16/029,126, filed on Jul 2018)
12. Computer Aided Traffic Enforcement Using Dense Correspondence Estimation with Multi-Level Metric Learning and Hierarchical Matching (application # US/16/029,167, filed on Jul 2018)
13. Landmark Localization on Objects in Images Using Convolutional Neural Networks (patent # US/10,289,934, granted on May 2019)
14. Advanced Driver-Assistance System with Landmark Localization on Objects in Images Using Convolutional Neural Networks (patent # US/10,289,935, granted on May 2019)
15. Surveillance System with Landmark Localization on Objects in Images Using Convolutional Neural Networks (patent # US/10,289,936, granted on May 2019)
16. Action Recognition System with Landmark Localization on Objects in Images Using Convolutional Neural Networks (patent # US/10,331,974, granted on May 2019)

Activities

• Conference Reviewers: International Conference on Computer Vision (ICCV), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), European Conference on Computer Vision (ECCV), International Conference on 3D Vision (3DV), IAPR Conference on Machine Vision Applications (MVA)
• Workshop Reviewers: Workshop on 3D Reconstruction in the Wild (3DRW) (in conjunction with ECCV/ICCV), Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues (VOCVALC) (in conjunction with CVPR)
• Journal Reviewers: International Journal of Computer Vision (IJCV), IEEE Transactions on Image Processing (TIP), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Journal of Computer Graphics Forum (CGF), Neurocomputing Journal (NEUCOM), The Visual Computer Journal (TVCJ), IEEE Journal of Selected Topics in Signal Processing (STSP), IEEE Signal Processing Letters (SPL)

Awards & Scholarships

• Dean's Commendation for Doctoral Thesis Excellence, 2014
• International Postgraduate Research Scholarship (IPRS), 2011-2014
• Australian Postgraduate Award (APA), 2011-2014
• Scholarship of Pony Chung Foundation - Hyundai Motor Company, 2009
• Bronze Medal in Vietnam National Olympiad in Informatics (VNOI), 2005
• Gold Medal in Mekong River Delta Olympiad in Informatics, 2005