1 citations · 2 across the 7 of their papers we have counts for
7 papers
SIEFormer: Spectral-Interpretable and -Enhanced Transformer for Generalized Category Discovery
Chunming Li, Shidong Wang, Tong Xin +1
This paper presents a novel approach, Spectral-Interpretable and -Enhanced Transformer (SIEFormer), which leverages spectral analysis to reinterpret the attention mechanism within…
Few-shot Novel Category Discovery
Chunming Li, Shidong Wang, Haofeng Zhang
The recently proposed Novel Category Discovery (NCD) adapt paradigm of transductive learning hinders its application in more real-world scenarios. In fact, few labeled data in part…
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation
Yuntian Bo, Yazhou Zhu, Lunbo Li +1
Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which lim…
RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation
Yazhou Zhu, Minxian Li, Qiaolin Ye +3
Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current…
Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation
Yazhou Zhu, Shidong Wang, Tong Xin +2
Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current…
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer
Yazhou Zhu, Shidong Wang, Tong Xin +1
Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulti…
Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation
Xiaojie Zhao, Yuming Shen, Shidong Wang +1
The recent advance in deep generative models outlines a promising perspective in the realm of Zero-Shot Learning (ZSL). Most generative ZSL methods use category semantic attributes…