Dual residual networks leveraging the potential of paired operations for image restoration X Liu, M Suganuma, Z Sun, T Okatani Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 230 | 2019 |
Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions M Suganuma, X Liu, T Okatani Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 103 | 2019 |
Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: Demonstration of … Y Bai, W Wu, Z Yang, J Yu, B Zhao, X Liu, H Yang, E Mas, S Koshimura Remote Sensing 13 (11), 2220, 2021 | 51 | 2021 |
Pyramid pooling module-based semi-siamese network: A benchmark model for assessing building damage from xBD satellite imagery datasets Y Bai, J Hu, J Su, X Liu, H Liu, X He, S Meng, E Mas, S Koshimura Remote Sensing 12 (24), 4055, 2020 | 41 | 2020 |
Feature quantization for defending against distortion of images Z Sun, M Ozay, Y Zhang, X Liu, T Okatani Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 32 | 2018 |
Nightlight as a proxy of economic indicators: Fine-grained GDP inference around Chinese mainland via attention-augmented CNN from daytime satellite imagery H Liu, X He, Y Bai, X Liu, Y Wu, Y Zhao, H Yang Remote Sensing 13 (11), 2067, 2021 | 26 | 2021 |
Integrating deep features for material recognition Y Zhang, M Ozay, X Liu, T Okatani 2016 23rd International Conference on Pattern Recognition (ICPR), 3697-3702, 2016 | 21 | 2016 |
Hd-fusion: Detailed text-to-3d generation leveraging multiple noise estimation J Wu, X Gao, X Liu, Z Shen, C Zhao, H Feng, J Liu, E Ding Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 16 | 2024 |
Matching in the dark: A dataset for matching image pairs of low-light scenes W Song, M Suganuma, X Liu, N Shimobayashi, D Maruta, T Okatani Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 16 | 2021 |
Cross-region domain adaptation for class-level alignment Z Wang, X Liu, M Suganuma, T Okatani arXiv preprint arXiv:2109.06422, 2021 | 9 | 2021 |
Removal of image obstacles for vehicle-mounted surrounding monitoring cameras by real-time video inpainting Y Hirohashi, K Narioka, M Suganuma, X Liu, Y Tamatsu, T Okatani Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 7 | 2020 |
Pushing the envelope of thin crack detection L Xu, T Hatsutani, X Liu, E Techapanurak, H Zou, T Okatani arXiv preprint arXiv:2101.03326, 2021 | 5 | 2021 |
Unsupervised domain adaptation for semantic segmentation via cross-region alignment Z Wang, X Liu, M Suganuma, T Okatani Computer Vision and Image Understanding 234, 103743, 2023 | 4 | 2023 |
Restoring images with unknown degradation factors by recurrent use of a multi-branch network X Liu, M Suganuma, X Luo, T Okatani arXiv preprint arXiv:1907.04508, 2019 | 4 | 2019 |
Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences X Liu, T Okatani arXiv preprint arXiv:1905.13560, 2019 | 3 | 2019 |
Learning deep representations of objects and materials for material recognition X Liu, M Ozay, Y Zhang, T Okatani Journal of Vision 16 (12), 177-177, 2016 | 3 | 2016 |
Joint Learning of Multiple Image Restoration Tasks X Liu, M Suganuma, T Okatani arXiv preprint arXiv:1907.04508, 2019 | 2 | 2019 |
Improving generalization ability of deep neural networks for visual recognition tasks T Okatani, X Liu, M Suganuma Computational Color Imaging: 7th International Workshop, CCIW 2019, Chiba …, 2019 | 1 | 2019 |
Supplementary material for “Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration” X Liu, M Suganuma, Z Sun, T Okatani | | |
Supplementary Material for “Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions” M Suganuma, X Liu, T Okatani | | |