{"url":"/method/cross-scale-non-local-attention","slug":"cross-scale-non-local-attention","name":"Cross-Scale Non-Local Attention","full_name":"Cross-Scale Non-Local Attention","full_name_withheld":false,"description_markdown":"**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 $p \\times p$ candidates in $Y$ to obtain the [softmax](https://paperswithcode.com/method/softmax) matching score. Finally, we conduct deconvolution.on the score by weighted adding the patches of size $\\left(sp, sp\\right)$ extracted from $X$. The obtained $Z$ of size $(sW, sH)$ will be $s$ times super-resolved than $X$.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2006.01424v1","title":"Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Attention Modules","url":"/methods/category/attention-modules","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/image-super-resolution-with-cross-scale-non","title":"Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining","date":"2020-06-02","arxiv_id":"2006.01424","n_code_links":3,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/feature-correlation","name":"Feature Correlation","papers":1},{"task":"/task/image-super-resolution","name":"Image Super-Resolution","papers":1},{"task":"/task/super-resolution","name":"Super-Resolution","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/cross-scale-non-local-attention"},"syntology_read_at":"2026-09-25T09:33:49+00:00"}