Context Encoding for Semantic Segmentation[CVPR2018]

更像是17年encoding network的一个应用，实验部分比较好
18,17

1.FCN framework

Good explanation

global receptive fields: conv(no linearities) + downsample

spatial resolution loss

• encoder: dilated conv
  
  • pro: expand receptive field
  • con: isolate pixels from context scene, misclassified
• decoder: upsample, deeplabv3+

multiple scale object

multi-resolution pyramid-based representation: SPP module
Q: Is capturing contextual information the same as increasing the receptive field size?

2.Architecture

Featuremap Attention

本文的核心贡献,　dense feature map经过一个encoding layer得到context embedding,然后通过FC得到一个classwise的score,作为权重(一种独特的Attention)

Semantic Encoding loss

实际上就是multi-label classification loss,分割网络加入一支classification loss可以提高结果
eg: Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation[IJCAI2017]

Encoding Layer

本文的基石,

对比

方法和传统方法的对比，以前使用bag of words或fisher vector, Dictionary一般通过聚类/GMM得到

步骤