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kwang_in_kimSmall


Associate Professor
School of Electrical & Computer Engineering
UNIST


Address:
E106 501-10, School of Electrical and Computer Engineering, UNIST
50 UNIST-gil, Eonyang-eup, Ulsan, 44919 Korea
email: kimki (at) unist.ac.kr


I am interested in advancing the understanding of how we can explore, make sense of, and interact with data. I contribute to this endeavor by exploiting and developing new techniques in machine learning, computer vision, computer graphics, and human-computer interaction.
My current research projects focus on algorithmic aspects of machine learning, especially in the context of neural networks, semi-supervised learning, active learning, multi-task learning, and transfer learning and on capturing, processing, mining, and visualizing image and video data.

Vita

Since 2019: Associate Professor, UNIST, School of Electrical & Computer Engineering
2016-2019: Senior Lecturer, University of Bath, Department of Computer Science
2013-2016: Lecturer, Lancaster University, School of Computing and Communications
2010-2013: Post-doc, Max Planck Institue for Informatics, GVV Group and Computer Graphics Department
2008-2009: Post-doc, Saarland University, Machine Learning Group
2002-2004, 2005-2008: Post-doc, Max Planck Institue for Biological Cybernetics, Empirical Inference Deptartment
2000-2002, 2004-2005: Post-doc, KAIST, A. I. Lab
2000: PhD, Kyungpook National University, Computer Engineering

Open positions


PhD and Masters in Machine Learning or Computer Vision


Applicants should have first-class Bachelor’s degree in Mathematics, Computer Science, Physics, Electrical Engineering, or a related subject field, and they must have a solid background in Mathematics including Linear Algebra, Vector Calculus, and Probability Theory, and have experience in programming, e.g., Python and Matlab.
If you are interested, please send me an email including your academic transcripts.
Please include `[PhD]’ or `[Masters]’ in the subject line.

Publications


K. I. Kim, C. Richardt, and H. J. Chang
Combining task predictors via enhancing joint predictability
Proc. ECCV 2020 (to appear)

Y. A. Mejjati, C. F. Gomes, K. I. Kim, E. Shechtman, and Z. Bylinskii
Look here! A parametric learning based approach to redirect visual attention
Proc. ECCV 2020 (to appear)

S. Baek, K. I. Kim, and T.-K. Kim
Weakly-supervised domain adaptation via GAN and mesh model for estimating 3D hand poses interacting objects
Proc. CVPR 2020
Paper | Supplemental


S. Kearney, W. Li, M. Parsons, K. I. Kim, and D. Cosker
RGBD-Dog: Predicting canine pose from RGBD sensors
Proc. CVPR 2020
Paper | Supplemental


D. Mehta, K. I. Kim, and C. Theobalt
On implicit filter level sparsity in convolutional neural networks
Proc. CVPR 2019
Paper | Supplemental


S. Baek, K. I. Kim, and T.-K. Kim
Pushing the envelope for RGB-based dense 3D hand pose estimation via neural rendering
Proc. CVPR 2019
Paper | Supplemental


K. I. Kim and H. J. Chang
Joint manifold diffusion for combining predictions on decoupled observations
Proc. CVPR 2019
Paper


Y. A. Mejjati, C. Richardt, J. Tompkin, D. Cosker, and K. I. Kim
Unsupervised attention-guided image-to-image translation
Proc. NeurIPS 2018
Paper | Webpage


A. Gokaslan, V. Ramanujan, D. Ritchie, K. I. Kim, and J. Tompkin
Improving shape deformation in unsupervised image-to-image translation
Proc. ECCV 2018
Paper


Y. Saquil, K. I. Kim, and P. Hall
Ranking CGANs: subjective control over semantic image attributes
Proc. BMVC 2018
Paper


J. Sock, K. I. Kim, C. Sahin, and T.-K. Kim
Multi-task deep networks for depth-based 6D object pose and joint registration in crowd scenarios
Proc. BMVC 2018
Paper


K. I. Kim, J. Park, and J. Tompkin
High-order tensor regularization with application to attribute ranking
Proc. CVPR 2018
Paper


Y. A. Mejjati, D. Cosker, and K. I. Kim
Multi-task learning by maximizing statistical dependence
Proc. CVPR 2018
Paper


S. Baek, K. I. Kim, and T.-K. Kim
Augmented skeleton space transfer for depth-based hand pose estimation
Proc. CVPR 2018
Paper


K. I. Kim, J. Tompkin, and C. Richardt
Predictor combination at test time
Proc. ICCV 2017
Paper | Supplemental


J. Tompkin, K. I. Kim, H. Pfister, and C. Theobalt
Criteria sliders: learning continuous database criteria via interactive ranking
Proc. BMVC 2017
PDF | Video


S. Baek, K. I. Kim, and T.-K. Kim
Real-time online action detection forests using spatiotemporal contexts
Proc. WACV 2017
Paper


K. I. Kim
Semi-supervised learning based on joint diffusion of graph functions and Laplacians
Proc. ECCV 2016
Webpage


Y. Zhang, T. Wilcockson, K. I. Kim, T. J. Crawford, H. G. Gellersen, and P. H. Sawyer
Monitoring dementia with automatic eye movements analysis
Proc. Intelligent Decision Technologies 2016
Paper


K. I. Kim, J. Tompkin, H. Pfister, and C. Theobalt
Context-guided diffusion for label propagation on graphs
Proc. ICCV 2015
Webpage


H. Rhodin, J. Tompkin, K. I. Kim, E. de Aguiar, H.-P. Seidel, and C. Theobalt
Generalizing wave gestures from sparse examples for real-time character control
ACM Trans. Graphics (Proc. SIGGRAPH Asia) 2015
Webpage


A. Elhayek, C. Stoll, K. I. Kim, and C. Theobalt
Outdoor human motion capture by simultaneous optimization of pose and camera parameters
Computer Graphics Forum 2015
Webpage


K. I. Kim, J. Tompkin, H. Pfister, and C. Theobalt
Local high-order regularization on data manifolds
Proc. CVPR 2015
Webpage


K. I. Kim, J. Tompkin, H. Pfister, and C. Theobalt
Semi-supervised learning with explicit relationship regularization
Proc. CVPR 2015
Webpage


Y. Kwon, K. I. Kim, J. Tompkin, J.-H. Kim, and C. Theobalt
Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes
IEEE Trans. PAMI 2015
Webpage


H. Rhodin, J. Tompkin, K. I. Kim, K. Varanasi, H.-P. Seidel, and C. Theobalt
Interactive motion mapping for real-time character control
Computer Graphics Forum (Proc. Eurographics) 2014
Webpage


K. I. Kim, J. Tompkin, and C. Theobalt
Curvature-aware regularization on Riemannian submanifolds
Proc. ICCV 2013
Webpage


M. Granados, K. I. Kim, J. Tompkin, and C. Theobalt
Automatic noise modeling for ghost-free HDR reconstruction
ACM Trans. Graphics (Proc. SIGGRAPH Asia) 2013
Webpage


J. Tompkin, M. H. Kim, K. I. Kim, J. Kautz, and C. Theobalt
Preference and artifact analysis for video transitions of places
ACM Trans. Applied Perception 2013
Webpage


F. Lenzen, K. I. Kim, R. Nair, S. Meister, H. Schäfer, F. Becker, C. Garbe, and C. Theobalt
Denoising strategies for time-of-flight data
Time-of-Flight Imaging: Algorithms, Sensors and Applications 2013
Paper


K. I. Kim, J. Tompkin, M. Theobald, J. Kautz, and C. Theobalt
Match graph construction for large image databases
Proc. ECCV 2012
Paper | Supplemental | Link prediction code MATLAB


M. Granados, K. I. Kim, J. Tompkin, J. Kautz, and C. Theobalt
Background inpainting for videos with dynamic objects and a free-moving camera
Proc. ECCV 2012
Webpage


Y. Kwon, K. I. Kim, J. H. Kim, and C. Theobalt
Efficient learning-based image enhancement: application to super-resolution and compression artifact removal
Proc. BMVC 2012
Webpage


J. Tompkin, K. I. Kim, J. Kautz, and C. Theobalt
Videoscapes: exploring sparse, unstructured video collections
ACM Trans. Graphics (Proc. SIGGRAPH) 2012
Webpage


A. Elhayek, C. Stoll, N. Hasler, K. I. Kim, H.-P. Seidel, and C. Theobalt
Spatio-temporal motion tracking with unsynchronized cameras
Proc. CVPR 2012
Paper | Video


A. Elhayek, C. Stoll, K. I. Kim, H.-P. Seidel, and C. Theobalt
Feature-based multi-video synchronization with subframe accuracy
Proc. DAGM-OAGM 2012
Paper | Supplemental


M. Granados, J. Tompkin, K. I. Kim, O. Grau, J. Kautz, and and C. Theobalt
How not to be seen – object removal from videos of crowded scenes
Computer Graphics Forum (Proc. Eurographics) 2012
Webpage


R. Herzog, M. Cadik, T. O. Aydin, K. I. Kim, K. Myszkowski, and H.-P. Seidel
NoRM: no-reference image quality metric for realistic image synthesis
Computer Graphics Forum (Proc. Eurographics) 2012
Webpage


K. I. Kim and Y. Kwon
Single-image super-resolution using sparse regression and natural image prior
IEEE Trans. PAMI 2010
Webpage


K. I. Kim, F. Steinke, and M. Hein
Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction
Proc. NIPS 2009
Webpage


P. Breuer, K. I. Kim, W. Kienzle, B. Schölkopf, and V. Blanz
Automatic 3D face reconstruction from single images or video
Proc. FG 2008
Paper


K. I. Kim and Y. Kwon
Example-based learning for single-image super-resolution and JPEG artifact removal
Max Planck Institute for Biological Cybernetics Technical Report No. 173 2008
Paper


C. Walder, K. I. Kim, and B. Schölkopf
Sparse multiscale Gaussian process regression
Proc. ICML 2008
Paper (long version)


K. I. Kim, K. Jung, and J. H. Kim
Fast color texture-based object detection in images: application to license plate localization
Proc. Workshop on Support Vector Machines: Theory and Applications 2005


K. I. Kim, M. O. Franz, and B. Schölkopf
Iterative kernel principal component analysis for image modeling
IEEE Trans. PAMI 2005


K. I. Kim, Y. Kwon, D. Kim, and J. H. Kim
Learning to remove JPEG artifacts
Proc. Korea Information Science Society Conference 2005


Publications before 2005