LiteBounD distills complementary semantic and boundary priors from multiple vision foundation models into compact segmentation backbones via dual-path and frequency-aware mechanisms, improving performance on both seen and unseen polyp datasets while preserving efficiency.
Meganet: Multi-scale edge-guided atten- tion network for weak boundary polyp segmentation,
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Sharpening Lightweight Models for Generalized Polyp Segmentation: A Boundary Guided Distillation from Foundation Models
LiteBounD distills complementary semantic and boundary priors from multiple vision foundation models into compact segmentation backbones via dual-path and frequency-aware mechanisms, improving performance on both seen and unseen polyp datasets while preserving efficiency.