Local vs Global Attention in ViT
Local vs global attention in ViT is the design tradeoff between restricted neighborhood focus and full image token interactions when building efficient transformer vision models - local attention reduces compute and often improves detail modeling, while global attention captures long-range relationships directly.
What Is the Local vs Global Attention Tradeoff?
- Local Attention: Each token attends to nearby patches inside a window.
- Global Attention: Each token attends to all tokens in the image sequence.
- Complexity Impact: Local patterns scale near linearly, global patterns scale quadratically.
- Model Behavior: Local improves fine textures, global improves scene-level context.
Why This Tradeoff Matters
- Scalability: High-resolution workloads are often impossible with pure global attention.
- Accuracy Balance: Pure local can miss distant dependencies, pure global can waste compute.
- Architecture Choice: Many modern backbones alternate local and occasional global blocks.
- Deployment Fit: Edge deployment often favors local windows with sparse global refresh.
- Task Specificity: Detection and segmentation usually need stronger local detail pathways.
Common Design Patterns
Windowed Local Blocks:
- Use fixed K x K windows for efficient neighborhood modeling.
- Add shifted windows between blocks to share cross-window context.
Periodic Global Blocks:
- Insert full attention at intervals to propagate global semantics.
- Maintains long-range coherence with bounded cost.
Hybrid Heads:
- Some heads attend locally while others attend globally in same layer.
- Improves representational diversity.
Practical Guidance
- High Resolution Inputs: Start with local attention baseline, then add sparse global layers.
- Global Context Tasks: Keep enough global blocks for scene-level reasoning.
- Profiling First: Measure FLOPs and memory before deciding hybrid depth ratio.
Local vs global attention in ViT is a central efficiency and quality lever that defines how a model spends its compute budget - good hybrid design delivers near-global understanding without quadratic runtime penalties.