{"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/fouriernet-compact-mask-representation-for","title":"FourierNet: Compact mask representation for instance segmentation using differentiable shape decoders","arxiv_id":"2002.02709","date":"2020-02-07","proceeding":null,"authors":["Hamd ul Moqeet Riaz","Nuri Benbarka","Andreas Zell"],"abstract":"We present FourierNet, a single shot, anchor-free, fully convolutional instance segmentation method that predicts a shape vector. Consequently, this shape vector is converted into the masks' contour points using a fast numerical transform. Compared to previous methods, we introduce a new training technique, where we utilize a differentiable shape decoder, which manages the automatic weight balancing of the shape vector's coefficients. We used the Fourier series as a shape encoder because of its coefficient interpretability and fast implementation. FourierNet shows promising results compared to polygon representation methods, achieving 30.6 mAP on the MS COCO 2017 benchmark. At lower image resolutions, it runs at 26.6 FPS with 24.3 mAP. It reaches 23.3 mAP using just eight parameters to represent the mask (note that at least four parameters are needed for bounding box prediction only). Qualitative analysis shows that suppressing a reasonable proportion of higher frequencies of Fourier series, still generates meaningful masks. These results validate our understanding that lower frequency components hold higher information for the segmentation task, and therefore, we can achieve a compressed representation. Code is available at: github.com/cogsys-tuebingen/FourierNet.","url_abs":"https://arxiv.org/abs/2002.02709v2","url_pdf":"https://arxiv.org/pdf/2002.02709v2.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":"fouriernet-compact-mask-representation-for","repo_url":"https://github.com/cogsys-tuebingen/FourierNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2002.02709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.02709"}},"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/cogsys-tuebingen/FourierNet","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"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":"91fa4ecdb663f148","entry":"get_mask_sample_region","repo":"cogsys-tuebingen/FourierNet","repo_kind":"official","path":"mmdet/models/anchor_heads/fouriernet_head.py","file_url":"https://github.com/cogsys-tuebingen/FourierNet/blob/HEAD/mmdet/models/anchor_heads/fouriernet_head.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"91fa4ecdb663f148"}},{"code_sha256_prefix":"056e7aa041cd689b","entry":"get_polar_coordinates","repo":"cogsys-tuebingen/FourierNet","repo_kind":"official","path":"mmdet/models/anchor_heads/fouriernet_head.py","file_url":"https://github.com/cogsys-tuebingen/FourierNet/blob/HEAD/mmdet/models/anchor_heads/fouriernet_head.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"056e7aa041cd689b"}},{"code_sha256_prefix":"b40e71b507ff5ddf","entry":"polar_centerness_target","repo":"cogsys-tuebingen/FourierNet","repo_kind":"official","path":"mmdet/models/anchor_heads/fouriernet_head.py","file_url":"https://github.com/cogsys-tuebingen/FourierNet/blob/HEAD/mmdet/models/anchor_heads/fouriernet_head.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b40e71b507ff5ddf"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}