Associate Professor
Computer Vision Laboratory
Dept. of Computer Science and Engineering
POSTECH (Pohang University of Science and Technology)
Pohang, Korea

Visiting Research Scientist
Venice, CA, USA


AAAI 2017:
(11/2016) We have two papers accepted to AAAI 2017.

MSRA Fellowship 2016:
(10/2016) Hyeonwoo was selected as one of the Microsoft Research Asia Fellows this year! The MSRA fellowship is a very competitive and prestigeous award, and Hyeonwoo is the second award winner in my lab, following Seunghoon in 2014.

Winning VOT 2016 Challenge:
(10/2016) We won the Visual Object Tracking (VOT) challenge with TCNN tracking algorithm this year! This is the back-to-back winning with MDNet in the last year challenge. Please refer to our arXiv paper our arXiv paper for the details. Congratulations, Hyeonseob and Mooyeol.

Sabbatical leave at Google
(08/2016) I am now with Google (LA) during my sabbatical leave for one year.

CVPR 2017:
(06/2016) I will be serving as an Area Chair for CVPR 2017.

Visual Question Answering and Visual Attention:
(06/2016) See our latest arXiv papers about visual question answering and visual attention.

CVPR 2016:
(03/2016) We have four papers accepted to CVPR 2016 (2 orals, 1 spotlight and 1 poster).

DPPnet for Image Question Answering:
(01/2016) The sourcecode of DPPnet paper is released in the project page.
(11/2015) We just released our arXiv paper about image question answering. This is a very unique and interesting approach based on dynamic parameter prediction for CNN. Check our paper.

Naver Faculty Award:
(01/2016) I received the Naver young faculty award.

MDNet for Visual Tracking:
(01/2016) The sourcecode of MDNet is released. Visit the project page.
(12/2015) I am very proud that MDNet won The Visual Object Tracking Challenge 2015 (VOT2015)! MDNet showed the best performance among 62 submitted or tested algorithms in the challenge. Read the result paper (Table 1 for summary) for the challenge details.
(10/2015) We obtained outstanding results of visual tracking using deep learning. Check our arXiv paper for details.

TransferNet: Transfer Learning for Semantic Segmentation:
(01/2016) The project page for TransferNet is open. Visit here.
(12/2015) Our paper about transfer learning for semantic segmentation is now available. Read our arXiv paper for details.

Research Interests

Computer vision, machine learning, deep learning


  • Ph.D. in Computer Science, University of Maryland, College Park, MD, USA, 12/2005
  • M.S. in Computer Engineering, Seoul National University, Seoul, Korea, 08/2000
  • B.S. in Computer Engineering, Seoul National University, Seoul, Korea, 02/1997

Selected Recent Publication [More]

Project Pages with Codes, Datasets and Results

  • Fast nearest neighbor search: [Paper], [Project]
  • Tracking with occlusion reasoning: [Paper], [Project]
  • Event detection: [Paper], [Project]
  • Generalized background subtraction (ECCV 2014): [Paper], [Code]
  • Generalized background subtraction (ICCV 2011): [Paper], [Project]
  • Joint human segmentation and pose tracking: [Paper], [Dataset]
  • Beyond chain models for visual tracking: [Project]
  • DeconvNet (deconvolution network for semantic segmentation): [Project]
  • DecoupledNet (semi-supervised semantic segmentation): [Project]
  • CNN-SVM (ICML 2015) [Project]
  • OGBDT tracking (ICCV 2015) [Project]


Professional Service

  • Conference/workshop organizer: ICCV 2019 (tutorial chair), DTCE 2012 (organizing chair), ACCV 2014 (demo chair)
  • Area chair: CVPR 2017, NIPS 2015, ICCV 2015, ACCV (2012, 2014, 2016), WACV (2014, 2017), ACML 2016
  • Program committee: NIPS 2016, CVPR (2007~2016), ICCV (2007, 2009, 2011, 2013), ECCV (2010, 2012, 2014), AISTATS (2017), ACCV (2009, 2010), AAAI (2016), IJCAI (2013), 3DV (2015), ICME (2013, 2014), PSIVT (2013)
  • Journal Associate Editor: Computer Vision and Image Understanding, Machine Vision and Applications, IPSJ Trans. on Computer Vision and Applications
  • Journal reviewer: TPAMI, IJCV, CVIU, TIP, IVC, TCSVT, TSP, TNNLS, T-IFS, JVCI, etc.

Selected Talks and Presentations [More]

  • CNN-based Visual Tracking, NVidia, CA, USA (11/2016)
  • Deep Weakly Supervised Learning in Computer Vision, Purdue University (09/2016), Korea-Japan Machine Learning Symposium (06/2016), KAIST (05/2016), UNIST (05/2016), KIAS (04/2016)
  • Learning Deconvolution Network for Semantic Segmentation, KAIST (04/2016), Yonsei University (03/2016), Naver (01/2016), Computer Vision Seminar at Univ. of Maryland, CSE Colloquium at Penn. State University, VASC Seminar in Robotics Institute at Carnegie Mellon University, GRASP Seminar at Univ. of Pennsylvania (10/2015), Willow Team at INRIA (07/2015), SNU (06/2015), Machine Learning Center Workshop at KCC (06/2015), UNIST (05/2015)
  • Deep Learning for Visual Recognition, SAIT (02/2016)
  • Deep Learning Architectures in Computer Vision Applications, EDA Workshop (02/2016)
  • Deep Learning for Visual Question Answering, IPIU2016 Tutorial (02/2016)
  • Structured Prediction using Convolutional Neural Networks, Deep Learning Workshop at SK Telecom (10/2015)
  • Deep Learning at POSTECH Computer Vision Lab., FCS Lab. at Samsung Electronics (10/2015), Hyundai Motors (08/2015)
  • Deconvolutions in Convolutional Neural Networks, Pattern Recognition and Machine Learning Summer School (08/2015)
  • Combinatorial Optimization in Computer Vision, IEEK Image Understanding Tutorial (08/2015)
  • Structured Prediction by SVM for Computer Vision Applications, Yonsei University (05/2013, 05/2015), IEEK Image Understanding Tutorial (08/2013, 11/2013), POSTECH (03/2013)
  • Deep Learning: Technologies and Applications, ImageNext (05/2015), SK-Planet (03/2015)
  • Pedestrian Detection: Shallow and Deep Learning, ETRI (01/2015)
  • Beyond Chain Models for Visual Tracking, SAIT (12/2014), ACCV Area Chair Workshop at NTU (09/2014), KCCV at SNU (08/2014)
  • Machine Learning for Visual Tracking, IEEK Image Understanding Tutorial (08/2014)
  • Spectral Clustering, Pattern Recognition and Machine Learning Summer School (PRMLSS) at Yonsei University (08/2014)
  • Tracking and Video Segmentation for Sports Broadcasting, The Korean Society of Broadcast Engineer Workshop (04/2014)
  • Recursive Bayesian Estimation: Applications to Visual Tracking, Pattern Recognition and Machine Learning Winter School (PRMLWS) at Yonsei University (04/2012)
  • Generalized Background Subtraction, SNU MAE (05/2012), DGIST (04/2011), SNU EE (05/2011), ETRI (07/2011), Hanyang University (01/2013, 01/2014)

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