9 citations · 12 across the 5 of their papers we have counts for
7 papers · 1 filter
Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification
Shulei Qiu, Wanqi Yang, Ming Yang
Our research focuses on few-shot fine-grained image classification, which faces two major challenges: appearance similarity of fine-grained objects and limited number of samples. T…
E2MPL:An Enduring and Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation
Wanqi Yang, Haoran Wang, Lei Wang +3
Few-shot unsupervised domain adaptation (FS-UDA) leverages a limited amount of labeled data from a source domain to enable accurate classification in an unlabeled target domain. De…
Multi-level Reliable Guidance for Unpaired Multi-view Clustering
Like Xin, Wanqi Yang, Lei Wang +1
In this thesis, we address the challenging problem of unpaired multi-view clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across…
Unpaired Multi-view Clustering via Reliable View Guidance
Like Xin, Wanqi Yang, Lei Wang +1
This paper focuses on unpaired multi-view clustering (UMC), a challenging problem where paired observed samples are unavailable across multiple views. The goal is to perform effect…
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…
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…