  Semantic segmentation is a challenge in scene parsing. It requires both
context information and rich spatial information. In this paper, we
differentiate features for scene segmentation based on dedicated attention
mechanisms (DF-DAM), and two attention modules are proposed to optimize the
high-level and low-level features in the encoder, respectively. Specifically,
we use the high-level and low-level features of ResNet as the source of context
information and spatial information, respectively, and optimize them with
attention fusion module and 2D position attention module, respectively. For
attention fusion module, we adopt dual channel weight to selectively adjust the
channel map for the highest two stage features of ResNet, and fuse them to get
context information. For 2D position attention module, we use the context
information obtained by attention fusion module to assist the selection of the
lowest-stage features of ResNet as supplementary spatial information. Finally,
the two sets of information obtained by the two modules are simply fused to
obtain the prediction. We evaluate our approach on Cityscapes and PASCAL VOC
2012 datasets. In particular, there aren't complicated and redundant processing
modules in our architecture, which greatly reduces the complexity, and we
achieving 82.3% Mean IoU on PASCAL VOC 2012 test dataset without pre-training
on MS-COCO dataset.
