RefineNet

Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

RCUMulti-resolution fusionChained residual poolingOutput convolutions

Contributor(s)

Initial contribute: 2020-03-08

Authorship

:  
Nanyang Technological University, Singapore.
:  
guosheng.lin{At}gmail.com
:  
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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

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

Last modifier
XU Kai
Last modify time
2020-12-18
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Contributor(s)

Initial contribute : 2020-03-08

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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
View History

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