local-global correspondence

**Local-Global Correspondence** is a **learning principle in self-supervised vision where the model is trained to predict global image properties from local patches** — ensuring that every part of the image encodes information about the whole, producing rich, hierarchical representations. **What Is Local-Global Correspondence?** - **Principle**: A small crop of an image (e.g., a cat's ear) should map to the same representation cluster as the full image (the complete cat). - **Implementation**: Cross-predict between local crops and global crops in the contrastive/distillation loss. - **Methods**: DINO, SwAV, and iBOT all leverage local-global correspondence. **Why It Matters** - **Semantic Features**: Encourages the model to learn semantic, part-aware representations rather than texture-only features. - **Dense Prediction**: Improves performance on downstream dense tasks (segmentation, detection) where local features must encode broader context. - **Emergent Properties**: DINO's ability to produce segmentation masks from attention maps is attributed to local-global correspondence training. **Local-Global Correspondence** is **the holographic principle for vision** — ensuring every pixel encodes something about the whole scene.