Research map / Label-efficient and robust learning / Knowledge distillation
SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models (CVPR 2025 · similarity 0.90)
- DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers (CVPR 2025 · similarity 0.90)
- Masking Teacher and Reinforcing Student for Distilling Vision-Language Models (CVPR 2026 · similarity 0.88)
- ScaleKD: Strong Vision Transformers Could Be Excellent Teachers (NeurIPS 2024 · similarity 0.88)
- Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning (ICLR 2025 · similarity 0.88)
- Hawaii: Hierarchical Visual Knowledge Transfer for Efficient Vision-Language Models (NeurIPS 2025 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
