  This paper proposes a novel attention model for semantic segmentation, which
aggregates multi-scale and context features to refine prediction. Specifically,
the skeleton convolutional neural network framework takes in multiple different
scales inputs, by which means the CNN can get representations in different
scales. The proposed attention model will handle the features from different
scale streams respectively and integrate them. Then location attention branch
of the model learns to softly weight the multi-scale features at each pixel
location. Moreover, we add an recalibrating branch, parallel to where location
attention comes out, to recalibrate the score map per class. We achieve quite
competitive results on PASCAL VOC 2012 and ADE20K datasets, which surpass
baseline and related works.
