| Unsupervised out-of-distribution detection by maximum classifier discrepancy Q Yu, K Aizawa Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 215 | 2019 |
| Multi-task curriculum framework for open-set semi-supervised learning Q Yu, D Ikami, G Irie, K Aizawa Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 144 | 2020 |
| Locoop: Few-shot out-of-distribution detection via prompt learning A Miyai, Q Yu, G Irie, K Aizawa Advances in Neural Information Processing Systems 36, 76298-76310, 2023 | 67 | 2023 |
| Divergence optimization for noisy universal domain adaptation Q Yu, A Hashimoto, Y Ushiku Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 34 | 2021 |
| Self-labeling framework for novel category discovery over domains Q Yu, D Ikami, G Irie, K Aizawa Proceedings of the AAAI Conference on Artificial Intelligence 36 (3), 3161-3169, 2022 | 31 | 2022 |
| Food image recognition by personalized classifier Q Yu, M Anzawa, S Amano, M Ogawa, K Aizawa 2018 25th IEEE international conference on image processing (ICIP), 171-175, 2018 | 31 | 2018 |
| Zero-shot in-distribution detection in multi-object settings using vision-language foundation models A Miyai, Q Yu, G Irie, K Aizawa arXiv preprint arXiv:2304.04521, 2023 | 22 | 2023 |
| Noisy localization annotation refinement for object detection J Mao, Q Yu, K Aizawa IEICE TRANSACTIONS on Information and Systems 104 (9), 1478-1485, 2021 | 17 | 2021 |
| Noisy annotation refinement for object detection J Mao, Q Yu, Y Yamakata, K Aizawa arXiv preprint arXiv:2110.10456, 2021 | 16 | 2021 |
| Unsolvable problem detection: Evaluating trustworthiness of vision language models A Miyai, J Yang, J Zhang, Y Ming, Q Yu, G Irie, Y Li, H Li, Z Liu, K Aizawa arXiv preprint arXiv:2403.20331, 2024 | 13 | 2024 |
| Generalized out-of-distribution detection and beyond in vision language model era: A survey A Miyai, J Yang, J Zhang, Y Ming, Y Lin, Q Yu, G Irie, S Joty, Y Li, H Li, ... arXiv preprint arXiv:2407.21794, 2024 | 11 | 2024 |
| Rethinking rotation in self-supervised contrastive learning: Adaptive positive or negative data augmentation A Miyai, Q Yu, D Ikami, G Irie, K Aizawa Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 11 | 2023 |
| CAN PRE-TRAINED NETWORKS DETECT FAMILIAR OUT-OF-DISTRIBUTION DATA? A Miyai, Q Yu, G Irie, K Aizawa arXiv preprint arXiv:2310.00847, 2023 | 8 | 2023 |
| Frame-level label refinement for skeleton-based weakly-supervised action recognition Q Yu, K Fujiwara Proceedings of the AAAI Conference on Artificial Intelligence 37 (3), 3322-3330, 2023 | 6 | 2023 |
| The aleatoric uncertainty estimation using a separate formulation with virtual residuals T Kawashima, Q Yu, A Asai, D Ikami, K Aizawa 2020 25th International Conference on Pattern Recognition (ICPR), 1438-1445, 2021 | 6 | 2021 |
| Unknown class label cleaning for learning with open-set noisy labels Q Yu, K Aizawa 2020 IEEE International Conference on Image Processing (ICIP), 1731-1735, 2020 | 6 | 2020 |
| Open-set domain adaptation with visual-language foundation models Q Yu, G Irie, K Aizawa Computer Vision and Image Understanding 250, 104230, 2025 | 5 | 2025 |
| Personalized food image classifier considering time-dependent and item-dependent food distribution Q Yu, M Anzawa, S Amano, K Aizawa IEICE TRANSACTIONS on Information and Systems 102 (11), 2120-2126, 2019 | 4 | 2019 |
| Qingyun Du C Wu, M Kang, F Zhao | 4 | 2006 |
| Exploring vision transformers for 3D human motion-language models with motion patches Q Yu, M Tanaka, K Fujiwara Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 2 | 2024 |
Kiyoharu AizawaUniversity of TokyoOverená e-mailová adresa na: hal.t.u-tokyo.ac.jp