3 papers
cs.CV2025
Bridging Granularity Gaps: Hierarchical Semantic Learning for Cross-domain Few-shot Segmentation
Sujun Sun, Haowen Gu, Cheng Xie +3
Cross-domain Few-shot Segmentation (CD-FSS) aims to segment novel classes from target domains that are not involved in training and have significantly different data distributions…
cs.CV2025
MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation
Yazhou Zhu, Haofeng Zhang
Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) is a potential solution for segmenting medical images with limited annotation using knowledge from other domains. The si…
cs.CV2025
Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation
Jianchao Jiang, Haofeng Zhang
Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. However, most existing prototype-b…