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Syst."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The semantic information can ensure better pixel classification, and the spatial information of the low-level feature map can ensure the detailed location of the pixels. However, this part of spatial information is often ignored in capturing semantic information, it is a huge loss for the spatial location of the image semantic category itself. To better alleviate this problem, we propose a Long and Short-Range Relevance Context Network. Specifically, we first construct a Long-Range Relevance Context Module to capture the global semantic context of the high-level feature and the ignored local spatial context information. At the same time, we build a Short-Range Relevance Context Module to capture the piecewise spatial context information in each stage of the low-level features in the form of jump connections. The whole network adopts a coding and decoding structure to better improve the segmentation results. Finally, we conduct a large number of experiments on three semantic segmentation datasets (PASCAL VOC2012, Cityscapes and ADE20K datasets) to verify the effectiveness of the network.<\/jats:p>","DOI":"10.1007\/s40747-023-01103-6","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T04:14:23Z","timestamp":1687320863000},"page":"7155-7170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Long and short-range relevance context network for semantic segmentation"],"prefix":"10.1007","volume":"9","author":[{"given":"Qing","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6281-9658","authenticated-orcid":false,"given":"Yongsheng","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Yuanhua","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Lintao","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"1103_CR1","first-page":"1","volume":"9","author":"Z Liu","year":"2022","unstructured":"Liu Z, Tong L, Chen L, Jiang Z, Zhou F, Zhang Q, Zhang X, Jin Y, Zhou H (2022) Deep learning based brain tumor segmentation: a survey. 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