Methods › General › Attention Modules › Cross-Scale Non-Local Attention
Cross-Scale Non-Local Attention
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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×p patches in X with the downsampled p ×p candidates in Y to obtain the softmax matching score. Finally, we conduct deconvolution.on the score by weighted adding the patches of size (sp, sp) extracted from X. The obtained Z of size (sW, sH) will be s times super-resolved than X.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining 2 Jun 2020 · 3 repositories · arXiv:2006.01424
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Feature Correlation | 1 |
| Image Super-Resolution | 1 |
| Super-Resolution | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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