{"url":"/method/stdc","slug":"stdc","name":"STDC","full_name":"Short-Term Dense Concatenate","full_name_withheld":false,"description_markdown":"**STDC**, or **Short-Term Dense Concatenate**, is a module for semantic segmentation to extract deep features with scalable\r\nreceptive field and multi-scale information. It aims to remove structure redundancy in the BiSeNet architecture, specifically BiSeNet adds an extra path to encode spatial information which can be time-consuming,. Instead, STDC gradually reduces the dimension of feature maps and use the aggregation of them for image representation.\r\n\r\nWe concatenate response maps from multiple continuous layers, each of which encodes input image/feature in different scales and respective fields, leading to multi-scale feature representation. To speed up, the filter size of layers is gradually reduced with negligible loss in segmentation performance.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Rethinking BiSeNet For Real-time Semantic Segmentation","paper":"/paper/rethinking-bisenet-for-real-time-semantic","first_author":"Mingyuan Fan","n_authors":7,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/rethinking-bisenet-for-real-time-semantic"},"source":{"url":"https://arxiv.org/abs/2104.13188v1","title":"Rethinking BiSeNet 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 Modules","url":"/methods/category/semantic-segmentation-modules","pwc_aliases":[]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":"/paper/make-a-strong-teacher-with-label-assistance-a","title":"Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic Segmentation","date":"2024-07-18","arxiv_id":"2407.13254","n_code_links":1,"syntology":null},{"paper":"/paper/lightweight-and-progressively-scalable","title":"Lightweight and Progressively-Scalable Networks for Semantic Segmentation","date":"2022-07-27","arxiv_id":"2207.13600","n_code_links":1,"syntology":null},{"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/rethinking-bisenet-for-real-time-semantic","title":"Rethinking BiSeNet For Real-time Semantic Segmentation","date":"2021-04-27","arxiv_id":"2104.13188","n_code_links":6,"syntology":{"ran":8,"of":16,"unverified":8,"pointer_only":0}}],"papers_shown":4,"tasks":[{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":4},{"task":"/task/real-time-semantic-segmentation","name":"Real-Time Semantic Segmentation","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/dichotomous-image-segmentation","name":"Dichotomous Image Segmentation","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":1},{"task":"/task/knowledge-distillation","name":"Knowledge Distillation","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":2},{"year":"2024","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/stdc"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}