The convolution operation suffers from a limited receptive filed, while
global modeling is fundamental to dense prediction tasks, such as semantic
segmentation. In this paper, we apply graph convolution into the semantic
segmentation task and propose an improved Laplacian. The graph reasoning is
directly performed in the original feature space organized as a spatial
pyramid. Different from existing methods, our Laplacian is data-dependent and
we introduce an attention diagonal matrix to learn a better distance metric. It
gets rid of projecting and re-projecting processes, which makes our proposed
method a light-weight module that can be easily plugged into current computer
vision architectures. More importantly, performing graph reasoning directly in
the feature space retains spatial relationships and makes spatial pyramid
possible to explore multiple long-range contextual patterns from different
scales. Experiments on Cityscapes, COCO Stuff, PASCAL Context and PASCAL VOC
demonstrate the effectiveness of our proposed methods on semantic segmentation.
We achieve comparable performance with advantages in computational and memory
overhead.

本文提出一种改进的拉普拉斯算子将图形卷积引入语义分割任务中，并在特征空间中直接执行图形推理，以保留空间关系，并从不同尺度探索多个远程上下文模式的空间金字塔，结果表明，该方法在减少计算和内存开销方面具有可比性