Attention to Refine through Multi-Scales for Semantic Segmentation
This work addresses semantic segmentation for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles semantic segmentation by proposing a novel attention model that aggregates multi-scale and context features to refine predictions, achieving competitive results on PASCAL VOC 2012 and ADE20K datasets.
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.