Shifted Window is the core mechanism of the Swin Transformer that enables cross-window communication — by shifting the window partition by half the window size between consecutive layers, tokens in one window can interact with tokens from adjacent windows through the shifted configuration.

How Does Shifted Window Work?

• Layer $l$: Partition image into non-overlapping $M imes M$ windows. Self-attention within each window.
• Layer $l+1$: Shift the partition by $(M/2, M/2)$ pixels. Now windows straddle the old boundaries.
• Effect: Tokens that were in separate windows at layer $l$ share a window at layer $l+1$.
• Efficient Masking: Cyclic shift + masked attention avoids padding overhead.
• Paper: Liu et al. (2021, Swin Transformer).

Why It Matters

• Cross-Window: Solves the communication problem of window-based attention without global tokens.
• Efficiency: $O(M^2 cdot N)$ vs. $O(N^2)$ for global attention. Linear in image size.
• SOTA: Swin Transformer became the dominant vision backbone (2021-2023) for classification, detection, segmentation.

Shifted Window is the sliding connection for windowed attention — a simple shift that enables information flow across window boundaries.