Fully Convolutional Networks for Semantic Segmentation
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...In this paper, we build upon a more elegant architecture, the so-called “fully convolutional network” [9]....
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...The main idea in [9] is to supplement a usual contracting network by successive layers, where pooling operators are replaced by upsampling operators....
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"Fully Convolutional Networks for Se..." refers background or methods in this paper
...If the output is downsampled by a factor of f , shift the input x pixels to the right and y pixels down, once for every (x, y) s.t. 0 ≤ x, y f ....
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...Sliding window detection by Sermanet et al. [32], semantic segmentation by Pinheiro and Collobert [31], and image restoration by Eigen et al. [6] do fully convolutional inference....
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...Next, we add skips between layers to fuse coarse, semantic and local, appearance information....
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...Each layer of data in a convnet is a three-dimensional array of size h × w × d, where h and w are spatial dimensions, and d is the feature or channel dimension....
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...Convnets are not only improving for whole-image classification [22, 34, 35], but also making progress on local tasks with structured output....
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