| Enhancement of SSD by concatenating feature maps for object detection J Jeong, H Park, N Kwak BMVC 2017, 2017 | 425* | 2017 |
| Consistency-based Semi-supervised Learning for Object detection J Jeong, S Lee, J Kim, N Kwak Advances in Neural Information Processing Systems, 10758-10767, 2019 | 406 | 2019 |
| Interpolation-based semi-supervised learning for object detection J Jeong, V Verma, M Hyun, J Kannala, N Kwak Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 73 | 2021 |
| Class-Imbalanced Semi-Supervised Learning M Hyun, J Jeong, N Kwak ICLRW 2021 (RobustML Workshop), 2020 | 56 | 2020 |
| Imposing Consistency for Optical Flow Estimation J Jeong, JM Lin, F Porikli, N Kwak Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 40 | 2022 |
| MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection JM Kim, J Jang, S Seo, J Jeong, J Na, N Kwak Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 36 | 2022 |
| Two-layer Residual Feature Fusion for Object Detection J Choi, K Lee, J Jeong, N Kwak ICPRAM 2019, 2017 | 33* | 2017 |
| Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction D Kim, G Lee, J Jeong, N Kwak AAAI 2020, 2019 | 20 | 2019 |
| Structural Similarity Index for Image Assessment Using Pixel Difference and Saturation Awareness J Jeong, YJ Kim Journal of KIISE 41 (10), 847-858, 2014 | 12 | 2014 |
| Self-Training using selection network for Semi-Supervised Learning J Jeong, S Lee, N Kwak ICPRAM 2020, 2020 | 8* | 2020 |
| Superpixel-based semantic segmentation trained by statistical process control H Park, J Jeong, Y Yoo, N Kwak BMVC 2017, 2017 | 7 | 2017 |
| Distractflow: Improving optical flow estimation via realistic distractions and pseudo-labeling J Jeong, H Cai, R Garrepalli, F Porikli Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 6 | 2023 |
| Mamo: Leveraging memory and attention for monocular video depth estimation R Yasarla, H Cai, J Jeong, Y Shi, R Garrepalli, F Porikli Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 5 | 2023 |
| Dift: Dynamic iterative field transforms for memory efficient optical flow R Garrepalli, J Jeong, RC Ravindran, JM Lin, F Porikli Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 5 | 2023 |
| Futuredepth: Learning to predict the future improves video depth estimation R Yasarla, MK Singh, H Cai, Y Shi, J Jeong, Y Zhu, S Han, R Garrepalli, ... arXiv preprint arXiv:2403.12953, 2024 | 1 | 2024 |
| Ocai: Improving optical flow estimation by occlusion and consistency aware interpolation J Jeong, H Cai, R Garrepalli, JM Lin, M Hayat, F Porikli Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 1 | 2024 |
| Supervised learning and occlusion masking for optical flow estimation JM Lin, J Jeong, FM Porikli US Patent App. 17/510,763, 2022 | 1 | 2022 |
| Scaling for depth estimation H Cai, ZHU Yinhao, J Jeong, SHI Yunxiao, FM Porikli US Patent App. 18/481,050, 2024 | | 2024 |
| Realistic distraction and pseudo-labeling regularization for optical flow estimation J Jeong, R Garrepalli, H Cai, FM Porikli US Patent App. 18/477,493, 2024 | | 2024 |
| SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations J Menjay Lin, J Jeong, H Cai, R Garrepalli, K Wang, F Porikli arXiv e-prints, arXiv: 2404.08135, 2024 | | 2024 |
Nojun KwakProfessor, Department of Intelligence and Information, Seoul National University在 snu.ac.kr 的电子邮件经过验证
