| Learning deconvolution network for semantic segmentation H Noh, S Hong, B Han Proceedings of the IEEE international conference on computer vision, 1520-1528, 2015 | 5255 | 2015 |
| 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 | 1034 | 2015 |
| 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 | 629 | 2017 |
| 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 | 486 | 2018 |
| Decoupled deep neural network for semi-supervised semantic segmentation S Hong, H Noh, B Han Advances in neural information processing systems 28, 2015 | 398 | 2015 |
| Diversity-sensitive conditional generative adversarial networks D Yang, S Hong, Y Jang, T Zhao, H Lee arXiv preprint arXiv:1901.09024, 2019 | 205 | 2019 |
| 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 | 204 | 2016 |
| 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 | 177 | 2017 |
| 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 | 141 | 2017 |
| The visual object tracking VOT2016 challenge results G Roffo, S Melzi Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 105 | 2016 |
| 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 | 91 | 2022 |
| Learning hierarchical semantic image manipulation through structured representations S Hong, X Yan, TS Huang, H Lee Advances in Neural Information Processing Systems 31, 2018 | 87 | 2018 |
| 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 | 81 | 2019 |
| 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 | 78 | 2021 |
| 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 | 74 | 2022 |
| 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 | 72 | 2020 |
| Online graph-based tracking H Nam, S Hong, B Han Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 51 | 2014 |
| Joint image clustering and labeling by matrix factorization S Hong, J Choi, J Feyereisl, B Han, LS Davis IEEE transactions on pattern analysis and machine intelligence 38 (7), 1411-1424, 2015 | 46 | 2015 |
| 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 | 43 | 2017 |
| Object recognition apparatus and method B Han, H Seunghoon, NOH Hyeonwoo US Patent 9,940,539, 2018 | 40 | 2018 |
Bohyung HanProfessor, Seoul National UniversityVerified email at snu.ac.kr
