11 citations · 23 across the 10 of their papers we have counts for
10 papers
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…
Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention
Yuquan Xie, Shengtao Peng, Wanqi Yang +2
Knowledge Tracing (KT) is a critical task in online education systems, aiming to monitor students' knowledge states throughout a learning period. Common KT approaches involve predi…
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…
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…
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…
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Yinghuan Shi, Wanqi Yang, Kim-Han Thung +5
In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD pat…
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…
Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning
Jinquan Sun, Yinghuan Shi, Yang Gao +4
Due to low tissue contrast, irregular object appearance, and unpredictable location variation, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is co…