Minyoung Kim
Minyoung Kim
Samsung AI Center Cambridge UK, SeoulTech, Rutgers University, Carnegie Mellon University
Verified email at scarletmail.rutgers.edu - Homepage
Title
Cited by
Cited by
Year
Face tracking and recognition with visual constraints in real-world videos
M Kim, S Kumar, V Pavlovic, H Rowley
2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008
4972008
Structured output ordinal regression for dynamic facial emotion intensity prediction
M Kim, V Pavlovic
European conference on computer vision, 649-662, 2010
572010
Gaussian Processes Multiple Instance Learning.
M Kim, F De la Torre
ICML, 535-542, 2010
442010
Discriminative learning for dynamic state prediction
M Kim, V Pavlovic
IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (10), 1847 …, 2009
272009
Dimensionality reduction using covariance operator inverse regression
M Kim, V Pavlovic
2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008
242008
Discriminative learning of mixture of bayesian network classifiers for sequence classification
M Kim, V Pavlovic
2006 IEEE Computer Society Conference on Computer Vision and Pattern …, 2006
232006
Correlation-based incremental visual tracking
M Kim
Pattern Recognition 45 (3), 1050-1060, 2012
212012
Central subspace dimensionality reduction using covariance operators
M Kim, V Pavlovic
IEEE transactions on pattern analysis and machine intelligence 33 (4), 657-670, 2011
202011
Large margin cost-sensitive learning of conditional random fields
M Kim
Pattern Recognition 43 (10), 3683-3692, 2010
202010
Semi-supervised learning of hidden conditional random fields for time-series classification
M Kim
Neurocomputing 119, 339-349, 2013
172013
Efficient kernel sparse coding via first-order smooth optimization
M Kim
IEEE transactions on neural networks and learning systems 25 (8), 1447-1459, 2014
162014
Model-induced term-weighting schemes for text classification
HK Kim, M Kim
Applied Intelligence 45 (1), 30-43, 2016
142016
Hidden conditional ordinal random fields for sequence classification
M Kim, V Pavlovic
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010
142010
Mixtures of conditional random fields for improved structured output prediction
M Kim
IEEE transactions on neural networks and learning systems 28 (5), 1233-1240, 2017
132017
Object tracking in video with visual constraints
M Kim, S Kumar, HA Rowley
US Patent 8,085,982, 2011
132011
Covariance operator based dimensionality reduction with extension to semi-supervised settings
M Kim, V Pavlovic
Artificial Intelligence and Statistics, 280-287, 2009
132009
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
M Kim, P Sahu, B Gholami, V Pavlovic
IEEE Conference on Computer Vision and Pattern Recognition, 2019
122019
Multiple instance learning via Gaussian processes
M Kim, F De la Torre
Data Mining and Knowledge Discovery 28 (4), 1078-1106, 2014
112014
Probabilistic sequence translation-alignment model for time-series classification
M Kim
IEEE Transactions on Knowledge and Data Engineering 26 (2), 426-437, 2014
112014
Sequence classification via large margin hidden Markov models
M Kim, V Pavlovic
Data Mining and Knowledge Discovery 23 (2), 322-344, 2011
112011
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Articles 1–20