FCN


FCN 是语义分割的开山之作，主要有两点：
1.全连接层换成卷积层
2.不同尺度的信息融合FCN-8s, 16s, 32s

 随着一次次的池化，感受野不断增大，语义信息不断增强，但是池化造成了像素位置信息的丢失。

 eg. 1/32大小的Heatmap上采样到原图之后，在Heatmap上如果偏移一个像素，在原图上就偏移了32个像素，这是不能容忍的。
因此，虽然前面的层语义信息较少，但是位置信息较多，作者就把1/8,1/16,1/32的三个层的输出融合起来。先把1/32的输出上采样到1/16，和pool4的输出做elementwise addition，结果在上采样到1/8, 和pool3的输出各个元素相加，得到1/8的结果，上采样8倍，求loss。

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