RefineNet

Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

RCUMulti-resolution fusionChained residual poolingOutput convolutions

true

Contributor(s)

Initial contribute: 2020-03-08

Authorship

:  
Nanyang Technological University, Singapore.
:  
guosheng.lin{At}gmail.com
:  
View
Is authorship not correct? Feed back

Classification(s)

Method-focused categoriesData-perspectiveIntelligent computation analysis

Detailed Description

English {{currentDetailLanguage}} English

Quote from:

    • https://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.pdf
    • CNNs have shown utstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation.
    • However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the
    • down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, introduce chained residual pooling, which captures rich background context in an efficient manner. Carry out comprehensive experiments and set new stateof-the-art results on seven public datasets. In particular, achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.

模型元数据

{{htmlJSON.HowtoCite}}

Guosheng Lin (2020). RefineNet, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/68bb26d9-3b79-40e2-9d61-d8a1ceb55cd9
{{htmlJSON.Copy}}

History

Last modifier
XU Kai
Last modify time
2020-12-18
Modify times

Contributor(s)

Initial contribute : 2020-03-08

{{htmlJSON.CoContributor}}

Authorship

:  
Nanyang Technological University, Singapore.
:  
guosheng.lin{At}gmail.com
:  
View
Is authorship not correct? Feed back

History

Last modifier
XU Kai
Last modify time
2020-12-18
Modify times

QR Code

×

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}









{{htmlJSON.RelatedItems}}

{{htmlJSON.LinkResourceFromRepositoryOrCreate}}{{htmlJSON.create}}.

Drop the file here, orclick to upload.
Select From My Space
+ add

{{htmlJSON.authorshipSubmitted}}

Cancel Submit
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Localizations}} + {{htmlJSON.Add}}
{{ item.label }} {{ item.value }}
{{htmlJSON.ModelName}}:
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
名称 别名 {{tag}} +
系列名 版本号 目的 修改内容 创建/修改日期 作者
摘要 详细描述
{{tag}} + 添加关键字
* 时间参考系
* 空间参考系类型 * 空间参考系名称

起始日期 终止日期 进展 开发者
* 是否开源 * 访问方式 * 使用方式 开源协议 * 传输方式 * 获取地址 * 发布日期 * 发布者



编号 目的 修改内容 创建/修改日期 作者





时间分辨率 时间尺度 时间步长 时间范围 空间维度 格网类型 空间分辨率 空间尺度 空间范围
{{tag}} +
* 类型
图例


* 名称 * 描述
示例描述 * 名称 * 类型 * 值/链接 上传


{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Add}} {{htmlJSON.Cancel}}

{{articleUploading.title}}

Authors:  {{articleUploading.authors[0]}}, {{articleUploading.authors[1]}}, {{articleUploading.authors[2]}}, et al.

Journal:   {{articleUploading.journal}}

Date:   {{articleUploading.date}}

Page range:   {{articleUploading.pageRange}}

Link:   {{articleUploading.link}}

DOI:   {{articleUploading.doi}}

Yes, this is it Cancel

The article {{articleUploading.title}} has been uploaded yet.

OK
{{htmlJSON.Cancel}} {{htmlJSON.Confirm}}