Deeplabv1 《Semanatic image segmentation with deep convolutional nets and fully connectd CRFS》

时间 2020-12-29

标签深度学习语义分割繁體版

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作者阐述了在DCNNs应用于图像任务时有两个技术难点需要攻克，一个是信号的降采样，另一个是空间不敏感。信号降采样主要是因为在计算过程中不断的pooling和downsampling，本文应用空洞卷积来解决。空间不敏感是由于在分类任务需要空间固定，所以限制了DCNNs模型的准确性。为解决这个问题作者采用了全连接的条件随机场，全连接CRF计算效率高，并且能够获得边缘细节信息。 Atrous

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1. 文章阅读：Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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6. 《DeepLab v1：semantic image segmentation with deep convolutional nets and fully connected CRFs》论文笔记
7. DeepLab-v2：Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully C
8. 论文阅读理解 - Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs
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