3 papers
cs.CV2026
MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention
Zhi Chen, Runze Hu, Le Zhang
Flow matching has recently emerged as a principled framework for learning continuous-time transport maps, enabling efficient ODE-based sampling without relying on stochastic diffus…
cs.CV2026
Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
Guohuan Xie, Xin He, Dingying Fan +3
Generalized few-shot semantic segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations. While diffusion models can synthesiz…
cs.CV2025
Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
Le Zhang, Fuping Wu, Arun Thirunavukarasu +3
Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especiall…