Polarized Self-Attention (PSA) is a dual-branch attention mechanism that computes channel-only and spatial-only self-attention in parallel — using slim tensors to maintain high resolution with minimal computational overhead.

How Does PSA Work?

• Channel Branch: Collapse spatial dimensions -> compute channel self-attention -> broadcast back.
• Spatial Branch: Collapse channel dimensions -> compute spatial self-attention -> broadcast back.
• Polarized: Each branch fully collapses the non-target dimension to a size of 1, creating "polarized" attention tensors.
• Combine: Element-wise multiplication or addition of both branches.
• Paper: Liu et al. (2021).

Why It Matters

• Fine-Grained: Maintains full spatial resolution and full channel resolution simultaneously.
• Efficient: The "polarization" (collapsing one dimension to 1) makes attention computation very cheap.
• Dense Prediction: Designed for pixel-level tasks (segmentation, keypoint detection) where resolution matters.

PSA is fully polarized dual attention — collapsing one dimension completely to compute the other efficiently, maintaining precise spatial and channel information.