2 papers
cs.CV2025
SPENet: Self-guided Prototype Enhancement Network for Few-shot Medical Image Segmentation
Chao Fan, Xibin Jia, Anqi Xiao +6
Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel classes of medical objects using only a few labeled images. Prototype-based methods have made significant progress…
cs.CV2025
Sample-aware RandAugment: Search-free Automatic Data Augmentation for Effective Image Recognition
Anqi Xiao, Weichen Yu, Hongyuan Yu
Automatic data augmentation (AutoDA) plays an important role in enhancing the generalization of neural networks. However, mainstream AutoDA methods often encounter two challenges:…