ViT Feature Maps
ViT feature maps are the spatial token representations extracted from intermediate transformer blocks that encode texture, part-level cues, and semantic layout - they provide the bridge between raw patch embeddings and downstream tasks such as classification, segmentation, and detection.
What Are ViT Feature Maps?
- Definition: Token grids reshaped into 2D maps at selected transformer depths.
- Representation Unit: Each token corresponds to one input patch and carries contextualized features.
- Depth Behavior: Early layers emphasize local edges, while deeper layers encode semantic object structure.
- Output Use: Feature maps can feed linear heads, decoder heads, or multi-scale fusion modules.
Why ViT Feature Maps Matter
- Task Transfer: Strong intermediate maps improve performance on dense prediction and retrieval.
- Debugging Signal: Layer level maps reveal whether model attention collapses or remains diverse.
- Architecture Design: Feature quality guides choices for depth, width, and patch size.
- Interpretability: Visualizing map activations helps explain model decisions to engineering teams.
- Efficiency: Selecting the right extraction layer avoids unnecessary decoder complexity.
How ViT Feature Maps Are Produced
Step 1:
- Image is patchified and projected into token embeddings.
- Positional information is added so tokens preserve spatial identity.
Step 2:
- Transformer blocks update token content through attention and MLP layers.
- Intermediate token sets are reshaped from N x C to H x W x C.
Step 3:
- Optional projection layers align channel dimensions for decoder or head inputs.
- Multi-layer fusion combines low-level detail with high-level semantics.
Best Practices
- Layer Selection: Extract from multiple depths instead of only final layer for dense tasks.
- Normalization: Apply consistent norm before feeding maps into external heads.
- Resolution Planning: Keep patch size aligned with required output granularity.
ViT feature maps are the working spatial memory of a transformer vision pipeline - when they are rich and well structured, downstream accuracy and explainability both improve substantially.