DANE: A Dual-level Alignment Network with Ensemble Learning for Multi-Source Domain Adaptation

Semantic-aware Adaptive Prompt Learning for Universal Multi-source Domain Adaptation

Y Yang, Y Hou, L Wen, P Zeng… - IEEE Signal Processing …, 2024 - ieeexplore.ieee.org
Universal multi-source domain adaptation (UniMDA) aims to transfer the knowledge from
multiple labeled source domains to an unlabeled target domain without constraints on the …
被引用次数：2 相关文章
[PDF] arxiv.org

Adaptive Prompt Learning with Negative Textual Semantics and Uncertainty Modeling for Universal Multi-Source Domain Adaptation

Y Yang, L Wen, Y Xu, J Zhou, Y Wang - arXiv preprint arXiv:2404.14696, 2024 - arxiv.org
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple
labeled source domains to an unlabeled target domain under domain shifts (different data …
被引用次数：1 相关文章 所有 2 个版本

Semi-supervised Domain Adaptation for Semantic Segmentation via Active Learning with Feature-and Semantic-Level Alignments

L Wen, Y Xu, Z Feng, J Zhou, L Zhou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) is a popular technique to reduce the manual
annotation cost in semantic segmentation. However, due to the absence of strong …
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