9 citations · 11 across the 2 of their papers we have counts for
3 papers
cs.CV2023
High-level semantic feature matters few-shot unsupervised domain adaptation
Lei Yu, Wanqi Yang, Shengqi Huang +2
In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conve…
cs.CV2021★ 9 cited
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
Shengqi Huang, Wanqi Yang, Lei Wang +2
This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the…
cs.CV2020★ 2 cited
Class Distribution Alignment for Adversarial Domain Adaptation
Wanqi Yang, Tong Ling, Chengmei Yang +4
Most existing unsupervised domain adaptation methods mainly focused on aligning the marginal distributions of samples between the source and target domains. This setting does not s…