Hyperbolic neural networks++ R Shimizu, Y Mukuta, T Harada arXiv preprint arXiv:2006.08210, 2020 | 155 | 2020 |
Common subspace for model and similarity: Phrase learning for caption generation from images Y Ushiku, M Yamaguchi, Y Mukuta, T Harada Proceedings of the IEEE international conference on computer vision, 2668-2676, 2015 | 74 | 2015 |
Multi-stage pathological image classification using semantic segmentation S Takahama, Y Kurose, Y Mukuta, H Abe, M Fukayama, A Yoshizawa, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 53 | 2019 |
Fully spiking variational autoencoder H Kamata, Y Mukuta, T Harada Proceedings of the AAAI conference on artificial intelligence 36 (6), 7059-7067, 2022 | 46 | 2022 |
Neural star domain as primitive representation Y Kawana, Y Mukuta, T Harada Advances in Neural Information Processing Systems 33, 7875-7886, 2020 | 28 | 2020 |
Backprop induced feature weighting for adversarial domain adaptation with iterative label distribution alignment T Westfechtel, HW Yeh, Q Meng, Y Mukuta, T Harada Proceedings of the IEEE/CVF winter conference on applications of computer …, 2023 | 22 | 2023 |
Finding and generating a missing part for story completion Y Mori, H Yamane, Y Mukuta, T Harada Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics …, 2020 | 22 | 2020 |
Modeling the variability of active galactic nuclei by an infinite mixture of Ornstein–Uhlenbeck (OU) processes T Takata, Y Mukuta, Y Mizumoto The Astrophysical Journal 869 (2), 178, 2018 | 20 | 2018 |
Spherical image generation from a single image by considering scene symmetry T Hara, Y Mukuta, T Harada Proceedings of the AAAI Conference on Artificial Intelligence 35 (2), 1513-1521, 2021 | 17 | 2021 |
Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries A Tejero-de-Pablos, K Huang, H Yamane, Y Kurose, Y Mukuta, J Iho, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 16 | 2019 |
Scalable generative models for graphs with graph attention mechanism W Kawai, Y Mukuta, T Harada arXiv preprint arXiv:1906.01861, 2019 | 15 | 2019 |
Deep modality invariant adversarial network for shared representation learning K Saito, Y Mukuta, Y Ushiku, T Harada Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 14* | 2017 |
Unsupervised pose-aware part decomposition for man-made articulated objects Y Kawana, Y Mukuta, T Harada European Conference on Computer Vision, 558-575, 2022 | 13 | 2022 |
Kernel approximation via empirical orthogonal decomposition for unsupervised feature learning Y Mukuta, T Harada Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 13 | 2016 |
Probabilistic partial canonical correlation analysis Y Mukuta International Conference on Machine Learning, 1449-1457, 2014 | 13 | 2014 |
Learning to evaluate humor in memes based on the incongruity theory K Tanaka, H Yamane, Y Mori, Y Mukuta, T Harada Proceedings of the Second Workshop on When Creative AI Meets Conversational …, 2022 | 11 | 2022 |
Rethinking task and metrics of instance segmentation on 3D point clouds K Arase, Y Mukuta, T Harada Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 10 | 2019 |
Long-term human video generation of multiple futures using poses N Fushishita, A Tejero-de-Pablos, Y Mukuta, T Harada Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 9 | 2020 |
Toward a better story end: Collecting human evaluation with reasons Y Mori, H Yamane, Y Mukuta, T Harada Proceedings of the 12th International Conference on Natural Language …, 2019 | 9 | 2019 |
Vinter: Image narrative generation with emotion-arc-aware transformer K Uehara, Y Mori, Y Mukuta, T Harada Companion Proceedings of the Web Conference 2022, 716-725, 2022 | 7 | 2022 |