| Maximum classifier discrepancy for unsupervised domain adaptation K Saito, K Watanabe, Y Ushiku, T Harada 2018 IEEE/CVF conference on computer vision and pattern recognition, 3723-3732, 2018 | 3110 | 2018 |
| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 2468* | 2024 |
| Neural 3d mesh renderer H Kato, Y Ushiku, T Harada 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3907-3916, 2018 | 1496 | 2018 |
| Strong-weak distribution alignment for adaptive object detection K Saito, Y Ushiku, T Harada, K Saenko 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2019 | 1100 | 2019 |
| MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes Q Ha, K Watanabe, T Karasawa, Y Ushiku, T Harada 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 1006 | 2017 |
| Open set domain adaptation by backpropagation K Saito, S Yamamoto, Y Ushiku, T Harada European conference on computer vision, 156-171, 2018 | 871 | 2018 |
| Asymmetric tri-training for unsupervised domain adaptation K Saito, Y Ushiku, T Harada International conference on machine learning, 2988-2997, 2017 | 836 | 2017 |
| Frequency-aware feature fusion for dense image prediction L Chen, Y Fu, L Gu, C Yan, T Harada, G Huang IEEE transactions on pattern analysis and machine intelligence 46 (12 …, 2024 | 448 | 2024 |
| You only need 90k parameters to adapt light: a light weight transformer for image enhancement and exposure correction Z Cui, K Li, L Gu, S Su, P Gao, Z Jiang, Y Qiao, T Harada arXiv preprint arXiv:2205.14871, 2022 | 439 | 2022 |
| Domain generalization using a mixture of multiple latent domains T Matsuura, T Harada Proceedings of the AAAI conference on artificial intelligence 34 (07), 11749 …, 2020 | 433 | 2020 |
| Learning from between-class examples for deep sound recognition Y Tokozume, Y Ushiku, T Harada arXiv preprint arXiv:1711.10282, 2017 | 394 | 2017 |
| Proceedings of the 3rd ACM International Conference on Multimedia in Asia C Chen, H Huang, J Zhou, T Harada, J Cai, W Liu, D Xu ACM, 2021 | 387* | 2021 |
| Adversarial dropout regularization K Saito, Y Ushiku, T Harada, K Saenko arXiv preprint arXiv:1711.01575, 2017 | 371 | 2017 |
| Between-class learning for image classification Y Tokozume, Y Ushiku, T Harada 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5486-5494, 2018 | 321 | 2018 |
| Learning environmental sounds with end-to-end convolutional neural network Y Tokozume, T Harada 2017 IEEE international conference on acoustics, speech and signal …, 2017 | 308 | 2017 |
| Lepard: Learning partial point cloud matching in rigid and deformable scenes Y Li, T Harada 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2022 | 301 | 2022 |
| Multitask aet with orthogonal tangent regularity for dark object detection Z Cui, GJ Qi, L Gu, S You, Z Zhang, T Harada 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2533-2542, 2021 | 294 | 2021 |
| Hyperbolic neural networks++ R Shimizu, Y Mukuta, T Harada arXiv preprint arXiv:2006.08210, 2020 | 286 | 2020 |
| Multispectral object detection for autonomous vehicles K Takumi, K Watanabe, Q Ha, A Tejero-De-Pablos, Y Ushiku, T Harada Proceedings of the on Thematic Workshops of ACM Multimedia 2017, 35-43, 2017 | 273 | 2017 |
| Neural articulated radiance field A Noguchi, X Sun, S Lin, T Harada 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5742-5752, 2021 | 271 | 2021 |
