| Domain-symmetric networks for adversarial domain adaptation Y Zhang, H Tang, K Jia, M Tan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 427 | 2019 |
| Unsupervised domain adaptation via structurally regularized deep clustering H Tang, K Chen, K Jia Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 341 | 2020 |
| Discriminative adversarial domain adaptation H Tang, K Jia Proceedings of the AAAI conference on artificial intelligence 34 (04), 5940-5947, 2020 | 208 | 2020 |
| Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data Y Zhang, H Tang, K Jia Proceedings of the european conference on computer vision (ECCV), 233-248, 2018 | 125 | 2018 |
| Unsupervised multi-class domain adaptation: Theory, algorithms, and practice Y Zhang, B Deng, H Tang, L Zhang, K Jia IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (5), 2775-2792, 2020 | 85 | 2020 |
| Geometry-aware self-training for unsupervised domain adaptation on object point clouds L Zou, H Tang, K Chen, K Jia Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 72 | 2021 |
| Unsupervised domain adaptation via distilled discriminative clustering H Tang, Y Wang, K Jia Pattern Recognition 127, 108638, 2022 | 43 | 2022 |
| Towards uncovering the intrinsic data structures for unsupervised domain adaptation using structurally regularized deep clustering H Tang, X Zhu, K Chen, K Jia, CLP Chen IEEE transactions on pattern analysis and machine intelligence 44 (10), 6517 …, 2021 | 26 | 2021 |
| A new benchmark: On the utility of synthetic data with blender for bare supervised learning and downstream domain adaptation H Tang, K Jia Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 18 | 2023 |
| Dual memory networks: A versatile adaptation approach for vision-language models Y Zhang, W Zhu, H Tang, Z Ma, K Zhou, L Zhang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2024 | 15 | 2024 |
| On universal black-box domain adaptation B Deng, Y Zhang, H Tang, C Ding, K Jia arXiv preprint arXiv:2104.04665, 2021 | 15 | 2021 |
| Towards discovering the effectiveness of moderately confident samples for semi-supervised learning H Tang, K Jia Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 14 | 2022 |
| Vicinal and categorical domain adaptation H Tang, K Jia Pattern Recognition 115, 107907, 2021 | 14 | 2021 |
| Stochastic consensus: Enhancing semi-supervised learning with consistency of stochastic classifiers H Tang, L Sun, K Jia European Conference on Computer Vision, 330-346, 2022 | 6 | 2022 |
| FITA: Fine-grained Image-Text Aligner for Radiology Report Generation H Yang, H Tang, X Li arXiv preprint arXiv:2405.00962, 2024 | | 2024 |
| Appendix for A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation H Tang, K Jia | | |
| Supplementary Material for “Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning” H Tang, K Jia | | |
| Supplementary for Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds L Zou, H Tang, K Chen, K Jia | | |
Kui Jia (贾奎)The Chinese University of Hong Kong, ShenzhenVerified email at cuhk.edu.cn
