  In this paper, we propose a simple but effective message passing method to
improve the boundary quality for the semantic segmentation result. Inspired by
the generated sharp edges of superpixel blocks, we employ superpixel to guide
the information passing within feature map. Simultaneously, the sharp
boundaries of the blocks also restrict the message passing scope. Specifically,
we average features that the superpixel block covers within feature map, and
add the result back to each feature vector. Further, to obtain sharper edges
and farther spatial dependence, we develop a multiscale superpixel module (MSP)
by a cascade of different scales superpixel blocks. Our method can be served as
a plug-and-play module and easily inserted into any segmentation network
without introducing new parameters. Extensive experiments are conducted on
three strong baselines, namely PSPNet, DeeplabV3, and DeepLabV3+, and four
challenging scene parsing datasets including ADE20K, Cityscapes, PASCAL VOC,
and PASCAL Context. The experimental results verify its effectiveness and
generalizability.

Computer Vision and Pattern Recognition