| Bi-directional cascade network for perceptual edge detection J He, S Zhang, M Yang, Y Shan, T Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 318 | 2019 |
| BDCN: Bi-directional cascade network for perceptual edge detection J He, S Zhang, M Yang, Y Shan, T Huang IEEE transactions on pattern analysis and machine intelligence 44 (1), 100-113, 2020 | 100 | 2020 |
| Multi-source domain adaptation with collaborative learning for semantic segmentation J He, X Jia, S Chen, J Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 80 | 2021 |
| Multi-target domain adaptation with collaborative consistency learning T Isobe, X Jia, S Chen, J He, Y Shi, J Liu, H Lu, S Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 78 | 2021 |
| Semi-supervised domain adaptation based on dual-level domain mixing for semantic segmentation S Chen, X Jia, J He, Y Shi, J Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 62 | 2021 |
| Atso: Asynchronous teacher-student optimization for semi-supervised image segmentation X Huo, L Xie, J He, Z Yang, W Zhou, H Li, Q Tian Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 54 | 2021 |
| T-svdnet: Exploring high-order prototypical correlations for multi-source domain adaptation R Li, X Jia, J He, S Chen, Q Hu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 39 | 2021 |
| Geometric anchor correspondence mining with uncertainty modeling for universal domain adaptation L Chen, Y Lou, J He, T Bai, M Deng Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 31 | 2022 |
| Can semantic labels assist self-supervised visual representation learning? L Wei, L Xie, J He, X Zhang, Q Tian Proceedings of the AAAI Conference on Artificial Intelligence 36 (3), 2642-2650, 2022 | 25 | 2022 |
| Evidential neighborhood contrastive learning for universal domain adaptation L Chen, Y Lou, J He, T Bai, M Deng Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6258-6267, 2022 | 22 | 2022 |
| Mutual nearest neighbor contrast and hybrid prototype self-training for universal domain adaptation L Chen, Q Du, Y Lou, J He, T Bai, M Deng Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6248-6257, 2022 | 13 | 2022 |
| Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning J Li, Y Bai, Y Lou, X Linghu, J He, S Xu, T Bai arXiv preprint arXiv:2207.01036, 2022 | 2 | 2022 |
| Switchable representation learning framework with self-compatibility S Wu, Y Bai, Y Lou, X Linghu, J He, LY Duan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 1 | 2023 |
| Bayesian evidential learning for few-shot classification X Linghu, Y Bai, Y Lou, S Wu, J Li, J He, T Bai arXiv preprint arXiv:2207.13137, 2022 | 1 | 2022 |
| Shift Pruning: Equivalent Weight Pruning for CNN via Differentiable Shift Operator T Niu, Y Lou, Y Teng, J He, Y Liu Proceedings of the 31st ACM International Conference on Multimedia, 5445-5454, 2023 | | 2023 |
| EAGER: Edge-Aided imaGe undERstanding System J He, X Liu, S Zhang Proceedings of the 2019 on International Conference on Multimedia Retrieval …, 2019 | | 2019 |
| Supplementary of “Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation” L Chen, Y Lou, J He, T Bai, M Deng | | |
| Supplemental Material for “Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation” S Chen, X Jia, J He, Y Shi, J Liu | | |
| Supplementary Material for Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation J He, X Jia, S Chen, J Liu | | |
Shiliang ZhangDepartment of Computer Science, School of EECS, Peking UniversityVerified email at pku.edu.cn
