paperswithcode.com
Cross-Scale Non-Local Attention Explained | Papers With Code
Cross-Scale Non-Local Attention, or CS-NL, is a non-local attention module for image super-resolution deep networks. It learns to mine long-range dependencies between LR features to larger-scale HR patches within the same feature map. Specifically, suppose we are conducting an s-scale super-resolution with the module, given a feature map $X$ of spatial size $(W, H)$, we first bilinearly downsample it to $Y$ with scale $s$, and match the $p\times p$ patches in $X$ with the downsampled $p \times p$ candidate
Похожие материалы на paperswithcode.com