| Learning deconvolution network for semantic segmentation H Noh, S Hong, B Han Proceedings of the IEEE international conference on computer vision, 1520-1528, 2015 | 5836 | 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 | 1084 | 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 | 729 | 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 | 594 | 2018 |
| Decoupled deep neural network for semi-supervised semantic segmentation S Hong, H Noh, B Han Advances in neural information processing systems 28, 2015 | 414 | 2015 |
| 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 | 246 | 2022 |
| Diversity-sensitive conditional generative adversarial networks D Yang, S Hong, Y Jang, T Zhao, H Lee arXiv preprint arXiv:1901.09024, 2019 | 246 | 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 | 219 | 2016 |
| 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 | 203 | 2022 |
| 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 | 189 | 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 | 159 | 2017 |
| The visual object tracking VOT2016 challenge results G Roffo, S Melzi Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 137 | 2016 |
| 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 | 119 | 2021 |
| 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 | 97 | 2019 |
| Learning hierarchical semantic image manipulation through structured representations S Hong, X Yan, TS Huang, H Lee Advances in Neural Information Processing Systems 31, 2018 | 95 | 2018 |
| 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 | 86 | 2021 |
| High-fidelity synthesis with disentangled representation W Lee, D Kim, S Hong, H Lee European conference on computer vision, 157-174, 2020 | 79 | 2020 |
| 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 | 53 | 2020 |
| 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 | 52 | 2017 |
| Transformers generalize deepsets and can be extended to graphs & hypergraphs J Kim, S Oh, S Hong Advances in Neural Information Processing Systems 34, 28016-28028, 2021 | 51 | 2021 |
Bohyung HanProfessor, Seoul National UniversityPotvrđena adresa e-pošte na snu.ac.kr
