语义分割--Attention to Scale: Scale-aware Semantic Image Segmentation
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Attention to Scale: Scale-aware Semantic Image Segmentation
CVPR2016
http://liangchiehchen.com/projects/DeepLab.html
针对语义分割问题，嵌入多尺度信息是很有必要的，这里我们提出用一个 attention mechanism 来学习每个像素位置的 softly weight the multi-scale features
attention model 学习对于不同尺度的物体赋予不同的权重
对于提取多尺度特征，目前主要有两种网络结构：
Skip-net 和 Share-net，这里我们认为 Share-net 能够与 attention model 更好的结合，所以采用了 attention model
怎么融合多尺度特征信息了？ 
这里我们首先得到权重，再根据权重来融合多尺度特征信息
PASCAL-Person-Part validation set 
E-Supv: extra supervision The ground truths are downsampled properly during training
max-pooling 和 attention model 效果对比：
PASCAL VOC 2012 test set
PASCAL VOC 2012 validation set
MS-COCO validation set
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