| Leveraging the invariant side of generative zero-shot learning J Li, M Jing, K Lu, Z Ding, L Zhu, Z Huang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 354 | 2019 |
| Locality preserving joint transfer for domain adaptation J Li, M Jing, K Lu, L Zhu, HT Shen IEEE Transactions on Image Processing 28 (12), 6103-6115, 2019 | 226 | 2019 |
| From zero-shot learning to cold-start recommendation J Li, M Jing, K Lu, L Zhu, Y Yang, Z Huang Proceedings of the AAAI conference on artificial intelligence 33 (01), 4189-4196, 2019 | 154 | 2019 |
| Faster domain adaptation networks J Li, M Jing, H Su, K Lu, L Zhu, HT Shen IEEE Transactions on Knowledge and Data Engineering 34 (12), 5770-5783, 2021 | 100 | 2021 |
| Alleviating feature confusion for generative zero-shot learning J Li, M Jing, K Lu, L Zhu, Y Yang, Z Huang Proceedings of the 27th ACM international conference on multimedia, 1587-1595, 2019 | 66 | 2019 |
| Adaptive component embedding for domain adaptation M Jing, J Zhao, J Li, L Zhu, Y Yang, HT Shen IEEE transactions on cybernetics 51 (7), 3390-3403, 2020 | 44 | 2020 |
| Incomplete cross-modal retrieval with dual-aligned variational autoencoders M Jing, J Li, L Zhu, K Lu, Y Yang, Z Huang Proceedings of the 28th ACM international conference on multimedia, 3283-3291, 2020 | 36 | 2020 |
| Balanced open set domain adaptation via centroid alignment M Jing, J Li, L Zhu, Z Ding, K Lu, Y Yang Proceedings of the AAAI conference on artificial intelligence 35 (9), 8013-8020, 2021 | 33 | 2021 |
| Investigating the bilateral connections in generative zero-shot learning J Li, M Jing, K Lu, L Zhu, HT Shen IEEE Transactions on Cybernetics 52 (8), 8167-8178, 2021 | 33 | 2021 |
| Challenging tough samples in unsupervised domain adaptation L Zuo, M Jing, J Li, L Zhu, K Lu, Y Yang Pattern Recognition 110, 107540, 2021 | 33 | 2021 |
| Learning modality-invariant latent representations for generalized zero-shot learning J Li, M Jing, L Zhu, Z Ding, K Lu, Y Yang Proceedings of the 28th ACM International Conference on multimedia, 1348-1356, 2020 | 32 | 2020 |
| Adversarial mixup ratio confusion for unsupervised domain adaptation M Jing, L Meng, J Li, L Zhu, HT Shen IEEE Transactions on Multimedia, 2022 | 27 | 2022 |
| Joint metric and feature representation learning for unsupervised domain adaptation Y Xie, Z Du, J Li, M Jing, E Chen, K Lu Knowledge-Based Systems 192, 105222, 2020 | 24 | 2020 |
| Multi-source domain adaptation with graph embedding and adaptive label prediction A Ma, F You, M Jing, J Li, K Lu Information Processing & Management 57 (6), 102367, 2020 | 19 | 2020 |
| Learning explicitly transferable representations for domain adaptation M Jing, J Li, K Lu, L Zhu, Y Yang Neural Networks 130, 39-48, 2020 | 16 | 2020 |
| Variational model perturbation for source-free domain adaptation M Jing, X Zhen, J Li, C Snoek Advances in Neural Information Processing Systems 35, 17173-17187, 2022 | 14 | 2022 |
| Open set domain adaptation via joint alignment and category separation J Liu, M Jing, J Li, K Lu, HT Shen IEEE Transactions on Neural Networks and Learning Systems 34 (9), 6186-6199, 2021 | 13 | 2021 |
| Learning distribution-matched landmarks for unsupervised domain adaptation M Jing, J Li, J Zhao, K Lu Database Systems for Advanced Applications: 23rd International Conference …, 2018 | 12 | 2018 |
| Adaptive component embedding for unsupervised domain adaptation M Jing, J Li, K Lu, J Liu, Z Huang 2019 IEEE International Conference on Multimedia and Expo (ICME), 1660-1665, 2019 | 3 | 2019 |
| Style-Guided Adversarial Teacher for Cross-Domain Object Detection L Jia, X Tian, Y Hu, M Jing, L Zuo, W Li Electronics 13 (5), 862, 2024 | 1 | 2024 |
Jingjing LiUniversity of Electronic Science and Technology of China在 uestc.edu.cn 的电子邮件经过验证
