Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation
Automated polyp segmentation in colonoscopy continues to pose challenges due to substantial appearance variations and indistinct polyp boundaries. Although emerging foundation models (FMs) such as DINOv2, SAM, and OneFormer, demonstrate remarkable generalization capabilities, their direct transfer to the polyp segmentation task and deployment in real-time clinical settings are difficult due to lack of large-scale labeled data and high computational demands. In addition, adopting multiple FMs together raises concerns, even though they encode complementary semantic and structural information. Wh
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- LinkedLinked via arxiv author · 85%Shivanshu Agnihotri →
“Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation”
- LinkedLinked via arxiv author · 85%Snehashis Majhi →
“Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation”
- LinkedLinked via arxiv author · 85%Deepak Ranjan Nayak →
“Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation”
- LinkedLinked via arxiv author · 85%Dwarikanath Mahapatra →
“Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation”
- LinkedLinked via arxiv author · 85%Debesh Jha →
“Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation”