| Cutmix: Regularization strategy to train strong classifiers with localizable features S Yun, D Han, SJ Oh, S Chun, J Choe, Y Yoo IEEE/CVF International Conference on Computer Vision (ICCV), 6023-6032, 2019 | 5456 | 2019 |
| Character region awareness for text detection Y Baek, B Lee, D Han, S Yun, H Lee IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9365-9374, 2019 | 1112 | 2019 |
| Deep pyramidal residual networks D Han, J Kim, J Kim IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5927-5935, 2017 | 857 | 2017 |
| What is wrong with scene text recognition model comparisons? dataset and model analysis J Baek, G Kim, J Lee, S Park, D Han, S Yun, SJ Oh, H Lee IEEE/CVF International Conference on Computer Vision (ICCV), 4715-4723, 2019 | 645 | 2019 |
| Rethinking spatial dimensions of vision transformers B Heo, S Yun, D Han, S Chun, J Choe, SJ Oh IEEE/CVF International Conference on Computer Vision (ICCV), 11936-11945, 2021 | 631 | 2021 |
| Salient region detection via high-dimensional color transform J Kim, D Han, YW Tai, J Kim IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 883-890, 2014 | 358 | 2014 |
| Ocr-free document understanding transformer G Kim, T Hong, M Yim, JY Nam, J Park, J Yim, W Hwang, S Yun, D Han, ... European Conference on Computer Vision (ECCV), 498-517, 2022 | 262 | 2022 |
| Salient region detection via high-dimensional color transform and local spatial support J Kim, D Han, YW Tai, J Kim IEEE Transactions on Image Processing (TIP) 25 (1), 9-23, 2015 | 187 | 2015 |
| Re-labeling imagenet: from single to multi-labels, from global to localized labels S Yun, SJ Oh, B Heo, D Han, J Choe, S Chun IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2340-2350, 2021 | 164 | 2021 |
| Adamp: Slowing down the slowdown for momentum optimizers on scale-invariant weights B Heo, S Chun, SJ Oh, D Han, S Yun, G Kim, Y Uh, JW Ha International Conference on Learning Representations (ICLR), 2020 | 164 | 2020 |
| Rethinking channel dimensions for efficient model design D Han, S Yun, B Heo, YJ Yoo IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 732-741, 2021 | 104 | 2021 |
| Vidt: An efficient and effective fully transformer-based object detector H Song, D Sun, S Chun, V Jampani, D Han, B Heo, W Kim, MH Yang International Conference on Learning Representations (ICLR), 2021 | 101 | 2021 |
| Unsupervised simultaneous orthogonal basis clustering feature selection D Han, J Kim IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5016-5023, 2015 | 97 | 2015 |
| Videomix: Rethinking data augmentation for video classification S Yun, SJ Oh, B Heo, D Han, J Kim arXiv preprint arXiv:2012.03457, 2020 | 75 | 2020 |
| An empirical evaluation on robustness and uncertainty of regularization methods S Chun, SJ Oh, S Yun, D Han, J Choe, Y Yoo arXiv preprint arXiv:2003.03879, 2020 | 60 | 2020 |
| Rexnet: Diminishing representational bottleneck on convolutional neural network D Han, S Yun, B Heo, YJ Yoo arXiv preprint arXiv:2007.00992, 2020 | 47 | 2020 |
| Extd: Extremely tiny face detector via iterative filter reuse YJ Yoo, D Han, S Yun arXiv preprint arXiv:1906.06579, 2019 | 47 | 2019 |
| Donut: Document understanding transformer without ocr G Kim, T Hong, M Yim, J Park, J Yim, W Hwang, S Yun, D Han, S Park arXiv preprint arXiv:2111.15664 7 (15), 2, 2021 | 43 | 2021 |
| Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling. S Seong, Y Lee, Y Kee, D Han, J Kim The Conference on Uncertainty in Artificial Intelligence (UAI), 1020-1030, 2018 | 40 | 2018 |
| C3: Concentrated-comprehensive convolution and its application to semantic segmentation H Park, Y Yoo, G Seo, D Han, S Yun, N Kwak arXiv preprint arXiv:1812.04920, 2018 | 37 | 2018 |
Sangdoo YunNAVER AI LabVerified email at navercorp.com
