| Accurate image super-resolution using very deep convolutional networks J Kim, JK Lee, KM Lee Proceedings of the IEEE conference on computer vision and pattern
, 2016 | 8551 | 2016 |
| Deeply-recursive convolutional network for image super-resolution J Kim, JK Lee, KM Lee Proceedings of the IEEE conference on computer vision and pattern
, 2016 | 3442 | 2016 |
| Learning to discover cross-domain relations with generative adversarial networks T Kim, M Cha, H Kim, JK Lee, J Kim International conference on machine learning, 1857-1865, 2017 | 2760 | 2017 |
| Continual learning with deep generative replay H Shin, JK Lee, J Kim, J Kim Advances in neural information processing systems 30, 2017 | 2395 | 2017 |
| Three dimensional image generating system and method accomodating multi-view imaging YJ Jung, H Wang, JW Kim, G Ma, X Mei, DS Park US Patent App. 13/100,905, 2011 | 117 | 2011 |
| A novel 2D-to-3D conversion technique based on relative height-depth cue YJ Jung, A Baik, J Kim, D Park Stereoscopic Displays and Applications XX 7237, 589-596, 2009 | 110 | 2009 |
| Fast adaptation to super-resolution networks via meta-learning S Park, J Yoo, D Cho, J Kim, TH Kim Computer VisionECCV 2020: 16th European Conference, Glasgow, UK, August 23
, 2020 | 96 | 2020 |
| Auto-meta: Automated gradient based meta learner search J Kim, S Lee, S Kim, M Cha, JK Lee, Y Choi, Y Choi, DY Cho, J Kim arXiv preprint arXiv:1806.06927, 2018 | 52 | 2018 |
| Unsupervised visual attribute transfer with reconfigurable generative adversarial networks T Kim, B Kim, M Cha, J Kim arXiv preprint arXiv:1707.09798, 2017 | 47 | 2017 |
| Apparatus, method and computer-readable medium generating depth map JW Kim, G Ma, H Wang, W Xiying, JY Kim, YJ Jung US Patent 8,553,972, 2013 | 40 | 2013 |
| Meta continual learning R Vuorio, DY Cho, D Kim, J Kim arXiv preprint arXiv:1806.06928, 2018 | 37 | 2018 |
| 2D-to-3D conversion by using visual attention analysis J Kim, A Baik, YJ Jung, D Park Stereoscopic Displays and Applications XXI 7524, 368-379, 2010 | 34 | 2010 |
| Method and apparatus for estimating depth, and method and apparatus for converting 2D video to 3D video A Baik, YJ Jung, JW Kim, D Park US Patent 9,137,512, 2015 | 31 | 2015 |
| Consistent depth maps recovery from a trinocular video sequence W Yang, G Zhang, H Bao, J Kim, HY Lee 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1466-1473, 2012 | 26 | 2012 |
| Method and apparatus for recovering depth information of image J Kim, G Zhang, DS Park, HY Lee, H Bao, W Yang US Patent 8,855,408, 2014 | 22 | 2014 |
| Learning to discover cross-domain relations with generative adversarial networks. arXiv 2017 T Kim, M Cha, H Kim, JK Lee, J Kim arXiv preprint arXiv:1703.05192, 0 | 22 | |
| Restore from restored: Video restoration with pseudo clean video S Lee, D Cho, J Kim, TH Kim Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
, 2021 | 20 | 2021 |
| Conversion device and method converting a two dimensional image to a three dimensional image JW Kim, YJ Jung, D Park, A Baik, YJ Jeong US Patent App. 12/801,514, 2010 | 17 | 2010 |
| Three-dimensional image generation apparatus and method using region extension of object in depth map YJ Jeong, YJ Jung, DS Park, AR Baik, JW Kim US Patent 10,163,246, 2018 | 16 | 2018 |
| Learning to embed semantic correspondence for natural language understanding S Jung, J Lee, J Kim Proceedings of the 22nd Conference on Computational Natural Language
, 2018 | 15 | 2018 |
