{"url":"/method/contextual-residual-aggregation","slug":"contextual-residual-aggregation","name":"Contextual Residual Aggregation","full_name":"Contextual Residual Aggregation","full_name_withheld":false,"description_markdown":"**Contextual Residual Aggregation**, or **CRA**, is a module for image inpainting. It can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Specifically, it involves a neural network to predict a low-resolution inpainted result and up-sample it to yield a large blurry image. Then we produce the high-frequency residuals for in-hole patches by aggregating weighted high-frequency residuals from contextual patches. Finally, we add the aggregated residuals to the large blurry image to obtain a sharp result.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting","paper":"/paper/contextual-residual-aggregation-for-ultra","first_author":"Zili Yi","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/contextual-residual-aggregation-for-ultra"},"source":{"url":"https://arxiv.org/abs/2005.09704v1","title":"Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Inpainting Modules","url":"/methods/category/image-inpainting-modules","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Model Blocks","url":"/methods/category/image-model-blocks","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/contextual-residual-aggregation-for-ultra","title":"Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting","date":"2020-05-19","arxiv_id":"2005.09704","n_code_links":6,"syntology":{"ran":2,"of":9,"unverified":7,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/2k","name":"2k","papers":1},{"task":null,"name":"8k","papers":1},{"task":null,"name":"GPU","papers":1},{"task":"/task/image-inpainting","name":"Image Inpainting","papers":1},{"task":"/task/high","name":"Vocal Bursts Intensity Prediction","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/contextual-residual-aggregation"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}