| Cutmix: Regularization strategy to train strong classifiers with localizable features S Yun, D Han, SJ Oh, S Chun, J Choe, Y Yoo IEEE International Conference on Computer Vision (ICCV), 2019 | 180 | 2019 |
| Attention-based Dropout Layer for Weakly Supervised Object Localization J Choe, H Shim IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 48 | 2019 |
| Face generation for low-shot learning using generative adversarial networks J Choe*, S Park*, K Kim*, JH Park*, D Kim*, H Shim IEEE International Conference on Computer Vision Workshop (ICCVW), 2017 | 44 | 2017 |
| Evaluating Weakly Supervised Object Localization Methods Right J Choe*, SJ Oh*, S Lee, S Chun, Z Akata, H Shim IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 | 16 | 2020 |
| An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods S Chun, SJ Oh, S Yun, D Han, J Choe, Y Yoo International Conference on Machine Learning Workshop (ICMLW), 2019 | 8 | 2019 |
| Improved techniques for weakly-supervised object localization J Choe, JH Park, H Shim arXiv preprint arXiv:1802.07888, 2018 | 4 | 2018 |
| Attention-based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation J Choe*, S Lee*, H Shim Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2020 | 2 | 2020 |
| Robust approach to inverse lighting using RGB-D images J Choe, H Shim Information Sciences 438, 73-94, 2018 | 1 | 2018 |
| Inverse lighting for non-homogeneous objects from color and depth image using wavelet representation J Choe, H Shim IS&T/SPIE Electronic Imaging, 2015 | 1 | 2015 |
| Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets J Choe*, SJ Oh*, S Chun, Z Akata, H Shim arXiv preprint arXiv:2007.04178, 2020 | | 2020 |
Hyunjung ShimAssociate Professor, Yonsei UniversityVerified email at yonsei.ac.kr
