{"url":"/method/flow-alignment-module","slug":"flow-alignment-module","name":"Flow Alignment Module","full_name":"Flow Alignment Module","full_name_withheld":false,"description_markdown":"**Flow Alignment Module**, or **FAM**, is a flow-based align module for scene parsing to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high resolution features effectively and efficiently. The concept of Semantic Flow is inspired from optical flow, which is widely used in video processing task to represent the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by relative motion. The authors postulate that the relatinship between two feature maps of arbitrary resolutions from the same image can also be represented with the “motion” of every pixel from one feature map to the other one. Once precise Semantic Flow is obtained, the network is able to propagate semantic features with minimal information loss.\r\n\r\nIn the FAM module, the transformed high-resolution feature map are combined with the low-resolution feature map to generate the semantic flow field, which is utilized to warp the low-resolution feature map to high-resolution feature map.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Semantic Flow for Fast and Accurate Scene Parsing","paper":"/paper/semantic-flow-for-fast-and-accurate-scene","first_author":"Xiangtai Li","n_authors":7,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/semantic-flow-for-fast-and-accurate-scene"},"source":{"url":"https://arxiv.org/abs/2002.10120v3","title":"Semantic Flow for Fast and Accurate Scene Parsing","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":"Semantic Segmentation Modules","url":"/methods/category/semantic-segmentation-modules","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/sfnet-faster-accurate-and-domain-agnostic","title":"SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow","date":"2022-07-10","arxiv_id":"2207.04415","n_code_links":1,"syntology":null},{"paper":"/paper/fast-and-accurate-scene-parsing-via-bi","title":"Fast and Accurate Scene Parsing via Bi-direction Alignment Networks","date":"2021-05-25","arxiv_id":"2105.11651","n_code_links":1,"syntology":null},{"paper":"/paper/semantic-flow-for-fast-and-accurate-scene","title":"Semantic Flow for Fast and Accurate Scene Parsing","date":"2020-02-24","arxiv_id":"2002.10120","n_code_links":6,"syntology":{"ran":1,"of":8,"unverified":7,"pointer_only":1}}],"papers_shown":3,"tasks":[{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":3},{"task":"/task/real-time-semantic-segmentation","name":"Real-Time Semantic Segmentation","papers":2},{"task":"/task/scene-parsing","name":"Scene Parsing","papers":2},{"task":"/task/optical-flow-estimation","name":"Optical Flow Estimation","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1},{"year":"2022","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/flow-alignment-module"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}