图像语义分割的方法和结构分析

Haozheng Ji
{"title":"图像语义分割的方法和结构分析","authors":"Haozheng Ji","doi":"10.1145/3481113.3481123","DOIUrl":null,"url":null,"abstract":"In recent years, due to the increasing demand for the understanding and recognition of content in images, image semantic segmentation technology has developed rapidly. Image semantic segmentation technology has also seen more and more reforms and innovations Each classical model has its own innovation and characteristics, which contributes to the development of image semantic segmentation.In this paper, four popular semantic segmentation models are reviewed and their characteristics are introduced.The results show that compared with other models, the SETR model based on Transformer has a higher performance level in semantic segmentation results.","PeriodicalId":112570,"journal":{"name":"2021 3rd International Symposium on Signal Processing Systems (SSPS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis on Approaches and Structures of Image Semantic Segmentation\",\"authors\":\"Haozheng Ji\",\"doi\":\"10.1145/3481113.3481123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, due to the increasing demand for the understanding and recognition of content in images, image semantic segmentation technology has developed rapidly. Image semantic segmentation technology has also seen more and more reforms and innovations Each classical model has its own innovation and characteristics, which contributes to the development of image semantic segmentation.In this paper, four popular semantic segmentation models are reviewed and their characteristics are introduced.The results show that compared with other models, the SETR model based on Transformer has a higher performance level in semantic segmentation results.\",\"PeriodicalId\":112570,\"journal\":{\"name\":\"2021 3rd International Symposium on Signal Processing Systems (SSPS)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd International Symposium on Signal Processing Systems (SSPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3481113.3481123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Symposium on Signal Processing Systems (SSPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3481113.3481123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

近年来,由于对图像内容的理解和识别需求的不断增加,图像语义分割技术得到了迅速发展。图像语义分割技术也出现了越来越多的改革和创新,每种经典模型都有自己的创新和特点,这有助于图像语义分割的发展。本文综述了四种常用的语义分割模型,并介绍了它们的特点。结果表明,与其他模型相比,基于Transformer的SETR模型在语义分割结果上具有更高的性能水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis on Approaches and Structures of Image Semantic Segmentation
In recent years, due to the increasing demand for the understanding and recognition of content in images, image semantic segmentation technology has developed rapidly. Image semantic segmentation technology has also seen more and more reforms and innovations Each classical model has its own innovation and characteristics, which contributes to the development of image semantic segmentation.In this paper, four popular semantic segmentation models are reviewed and their characteristics are introduced.The results show that compared with other models, the SETR model based on Transformer has a higher performance level in semantic segmentation results.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信