{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recurrent-feature-reasoning-for-image-1","title":"Recurrent Feature Reasoning for Image Inpainting","arxiv_id":"2008.03737","date":"2020-08-09","proceeding":"CVPR 2020 6","authors":["Jingyuan Li","Ning Wang","Lefei Zhang","Bo Du","DaCheng Tao"],"abstract":"Existing inpainting methods have achieved promising performance for recovering regular or small image defects. However, filling in large continuous holes remains difficult due to the lack of constraints for the hole center. In this paper, we devise a Recurrent Feature Reasoning (RFR) network which is mainly constructed by a plug-and-play Recurrent Feature Reasoning module and a Knowledge Consistent Attention (KCA) module. Analogous to how humans solve puzzles (i.e., first solve the easier parts and then use the results as additional information to solve difficult parts), the RFR module recurrently infers the hole boundaries of the convolutional feature maps and then uses them as clues for further inference. The module progressively strengthens the constraints for the hole center and the results become explicit. To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR. Empirically, we first compare the proposed RFR-Net with existing backbones, demonstrating that RFR-Net is more efficient (e.g., a 4\\% SSIM improvement for the same model size). We then place the network in the context of the current state-of-the-art, where it exhibits improved performance. The corresponding source code is available at: https://github.com/jingyuanli001/RFR-Inpainting","url_abs":"https://arxiv.org/abs/2008.03737v1","url_pdf":"https://arxiv.org/pdf/2008.03737v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recurrent-feature-reasoning-for-image-1","repo_url":"https://github.com/jingyuanli001/RFR-Inpainting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2008.03737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.03737"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jingyuanli001/RFR-Inpainting","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"2313fcb77332018c","entry":"generate_stroke_mask","repo":"jingyuanli001/RFR-Inpainting","repo_kind":"official","path":"dataset.py","file_url":"https://github.com/jingyuanli001/RFR-Inpainting/blob/HEAD/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2313fcb77332018c"}},{"code_sha256_prefix":"c333d34b2f3803eb","entry":"get_state_dict_on_cpu","repo":"jingyuanli001/RFR-Inpainting","repo_kind":"official","path":"utils/io.py","file_url":"https://github.com/jingyuanli001/RFR-Inpainting/blob/HEAD/utils/io.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c333d34b2f3803eb"}},{"code_sha256_prefix":"6494155d4a61448e","entry":"load_ckpt","repo":"jingyuanli001/RFR-Inpainting","repo_kind":"official","path":"utils/io.py","file_url":"https://github.com/jingyuanli001/RFR-Inpainting/blob/HEAD/utils/io.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6494155d4a61448e"}},{"code_sha256_prefix":"e32ed92be303826d","entry":"np_free_form_mask","repo":"jingyuanli001/RFR-Inpainting","repo_kind":"official","path":"dataset.py","file_url":"https://github.com/jingyuanli001/RFR-Inpainting/blob/HEAD/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e32ed92be303826d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}