| Enhanced deep residual networks for single image super-resolution B Lim, S Son, H Kim, S Nah, K Mu Lee Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 7897 | 2017 |
| Ntire 2017 challenge on single image super-resolution: Methods and results R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2226 | 2017 |
| Channel attention is all you need for video frame interpolation M Choi, H Kim, B Han, N Xu, KM Lee Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 10663 …, 2020 | 329 | 2020 |
| Meta-learning with task-adaptive loss function for few-shot learning S Baik, J Choi, H Kim, D Cho, J Min, KM Lee Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 152 | 2021 |
| Meta-Learning with Adaptive Hyperparameters S Baik, M Choi, J Choi, H Kim, KM Lee Advances in Neural Information Processing Systems, 2020, 2020 | 152 | 2020 |
| Task-aware image downscaling H Kim, M Choi, B Lim, KM Lee Proceedings of the European conference on computer vision (ECCV), 399-414, 2018 | 106 | 2018 |
| Real-time video super-resolution on smartphones with deep learning, mobile ai 2021 challenge: Report A Ignatov, A Romero, H Kim, R Timofte Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 69 | 2021 |
| Daq: Channel-wise distribution-aware quantization for deep image super-resolution networks C Hong, H Kim, S Baik, J Oh, KM Lee Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 54 | 2022 |
| Machine learning-based predictive modeling of depression in hypertensive populations C Lee, H Kim PLoS One 17 (7), e0272330, 2022 | 41 | 2022 |
| Cadyq: Content-aware dynamic quantization for image super-resolution C Hong, S Baik, H Kim, S Nah, KM Lee European Conference on Computer Vision, 367-383, 2022 | 38 | 2022 |
| Learning to learn task-adaptive hyperparameters for few-shot learning S Baik, M Choi, J Choi, H Kim, KM Lee IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (3), 1441-1454, 2023 | 34 | 2023 |
| Motion-aware dynamic architecture for efficient frame interpolation M Choi, S Lee, H Kim, KM Lee Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 31 | 2021 |
| Attentive fine-grained structured sparsity for image restoration J Oh, H Kim, S Nah, C Hong, J Choi, KM Lee Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 23 | 2022 |
| AIM 2019 challenge on video temporal super-resolution: Methods and results S Nah, S Son, R Timofte, KM Lee, L Siyao, Z Pan, X Xu, W Sun, M Choi, ... 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW …, 2019 | 20 | 2019 |
| Fine-grained neural architecture search for image super-resolution H Kim, S Hong, B Han, H Myeong, KM Lee Journal of Visual Communication and Image Representation 89, 103654, 2022 | 16* | 2022 |
| Batch normalization tells you which filter is important J Oh, H Kim, S Baik, C Hong, KM Lee Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 12 | 2022 |
| Searching for controllable image restoration networks H Kim, S Baik, M Choi, J Choi, KM Lee Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 9 | 2021 |
| Learning Controllable ISP for Image Enhancement H Kim, KM Lee IEEE Transactions on Image Processing, 2023 | 4* | 2023 |
| NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs H Kim, KM Lee The Eleventh International Conference on Learning Representations, 2023 | 1 | 2023 |
Kyoung Mu LeeProfessor, Department of Electrical and Computer Engineering, Seoul National UniversityVerified email at snu.ac.kr