Multi-Axis Vision Transformer (MaxViT) is an advanced vision transformer architecture that combines local and global attention mechanisms for efficient image processing. It introduces a multi-axis attention mechanism to improve performance and efficiency.
Key Concepts in MaxViT
1️⃣ Grid and Block Attention (Multi-Axis Mechanism)
Block Attention → Captures local features within small windows (like Swin Transformer).
Grid Attention → Captures global features across the entire image by selecting spatially distant tokens.
This allows both fine-grained and large-scale context awareness.
2️⃣ Hierarchical Structure
Similar to CNN-based architectures, MaxViT reduces the image resolution gradually while increasing feature depth.
3️⃣ Efficient Attention Computation
Instead of computing self-attention on the full image, MaxViT splits it into smaller patches (like Swin Transformer) but applies multi-axis attention for better scalability.
This reduces complexity compared to ViT while keeping strong performance.
4️⃣ Scalability & Performance
Works well for classification, detection, and segmentation.
Outperforms ViT and Swin Transformer on large-scale datasets (like ImageNet).
Why is Multi-Axis Attention Useful?
✅ Captures both local & global dependencies efficiently.
✅ Less computational cost than full self-attention in standard ViT.
✅ Works well on high-resolution images.
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