{"url":"/method/bisenet-v2","slug":"bisenet-v2","name":"BiSeNet V2","full_name":"BiSeNet V2","full_name_withheld":false,"description_markdown":"**BiSeNet V2** is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic Branch simply requires a large receptive field to capture semantic context, while the detail information can be supplied by the Detail Branch. Therefore, the Semantic Branch can be made very lightweight with fewer channels and a fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation.","description_state":"present","introduced_year":null,"introduced_by":{"title":"BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation","paper":"/paper/bisenet-v2-bilateral-network-with-guided","first_author":"Changqian Yu","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/bisenet-v2-bilateral-network-with-guided"},"source":{"url":"https://arxiv.org/abs/2004.02147v1","title":"BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation","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 Models","url":"/methods/category/semantic-segmentation-models","pwc_aliases":["segmentation-models"]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"Exploring Lip Segmentation Techniques in Computer Vision: A Comparative Analysis","date":"2023-11-20","arxiv_id":"2311.11992","n_code_links":0,"syntology":null},{"paper":"/paper/bisenet-v2-bilateral-network-with-guided","title":"BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation","date":"2020-04-05","arxiv_id":"2004.02147","n_code_links":7,"syntology":{"ran":6,"of":10,"unverified":4,"pointer_only":5}}],"papers_shown":2,"tasks":[{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/edge-computing","name":"Edge-computing","papers":1},{"task":"/task/lip-reading","name":"Lip Reading","papers":1},{"task":"/task/real-time-semantic-segmentation","name":"Real-Time Semantic Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2020","papers":1},{"year":"2023","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/bisenet-v2"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}