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.
Cross- level feature aggregation network for polyp segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ASGNet combines a spectrum-guided non-local perception module, multi-source semantic extractor, and dense cross-layer decoder to outperform 21 prior methods on five polyp segmentation benchmarks.
citing papers explorer
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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.
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ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation
ASGNet combines a spectrum-guided non-local perception module, multi-source semantic extractor, and dense cross-layer decoder to outperform 21 prior methods on five polyp segmentation benchmarks.