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Seunghoon Hong
Seunghoon Hong
Associate Professor, KAIST
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Learning deconvolution network for semantic segmentation
H Noh, S Hong, B Han
Proceedings of the IEEE international conference on computer vision, 1520-1528, 2015
57642015
Online tracking by learning discriminative saliency map with convolutional neural network
S Hong, T You, S Kwak, B Han
International conference on machine learning, 597-606, 2015
10752015
Decomposing motion and content for natural video sequence prediction
R Villegas, J Yang, S Hong, X Lin, H Lee
arXiv preprint arXiv:1706.08033, 2017
7032017
Inferring semantic layout for hierarchical text-to-image synthesis
S Hong, D Yang, J Choi, H Lee
Proceedings of the IEEE conference on computer vision and pattern ¡¦, 2018
5902018
Decoupled deep neural network for semi-supervised semantic segmentation
S Hong, H Noh, B Han
Advances in neural information processing systems 28, 2015
4122015
Diversity-sensitive conditional generative adversarial networks
D Yang, S Hong, Y Jang, T Zhao, H Lee
arXiv preprint arXiv:1901.09024, 2019
2422019
Learning transferrable knowledge for semantic segmentation with deep convolutional neural network
S Hong, J Oh, H Lee, B Han
Proceedings of the IEEE conference on computer vision and pattern ¡¦, 2016
2222016
Part-based pseudo label refinement for unsupervised person re-identification
Y Cho, WJ Kim, S Hong, SE Yoon
Proceedings of the IEEE/CVF conference on computer vision and pattern ¡¦, 2022
2172022
Weakly supervised semantic segmentation using web-crawled videos
S Hong, D Yeo, S Kwak, H Lee, B Han
Proceedings of the IEEE Conference on Computer Vision and Pattern ¡¦, 2017
1872017
Pure transformers are powerful graph learners
J Kim, D Nguyen, S Min, S Cho, M Lee, H Lee, S Hong
Advances in Neural Information Processing Systems 35, 14582-14595, 2022
1702022
The visual object tracking vot2016 challenge results
G Roffo, S Melzi
Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 ¡¦, 2016
167*2016
Weakly supervised semantic segmentation using superpixel pooling network
S Kwak, S Hong, B Han
Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017
1562017
Improving unsupervised image clustering with robust learning
S Park, S Han, S Kim, D Kim, S Park, S Hong, M Cha
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ¡¦, 2021
1092021
Adversarial defense via learning to generate diverse attacks
Y Jang, T Zhao, S Hong, H Lee
Proceedings of the IEEE/CVF International Conference on Computer Vision ¡¦, 2019
952019
Learning hierarchical semantic image manipulation through structured representations
S Hong, X Yan, TS Huang, H Lee
Advances in Neural Information Processing Systems 31, 2018
952018
Setvae: Learning hierarchical composition for generative modeling of set-structured data
J Kim, J Yoo, J Lee, S Hong
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ¡¦, 2021
852021
High-fidelity synthesis with disentangled representation
W Lee, D Kim, S Hong, H Lee
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 ¡¦, 2020
762020
Weakly supervised learning with deep convolutional neural networks for semantic segmentation: Understanding semantic layout of images with minimum human supervision
S Hong, S Kwak, B Han
IEEE Signal Processing Magazine 34 (6), 39-49, 2017
532017
Online graph-based tracking
H Nam, S Hong, B Han
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland ¡¦, 2014
512014
Variational interaction information maximization for cross-domain disentanglement
HJ Hwang, GH Kim, S Hong, KE Kim
Advances in Neural Information Processing Systems 33, 22479-22491, 2020
472020
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