Spatial Pyramid Based Graph Reasoning for Semantic Segmentation

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 dir...

Full description

Saved in:
Bibliographic Details
Published in2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 8947 - 8956
Main Authors Li, Xia, Yang, Yibo, Zhao, Qijie, Shen, Tiancheng, Lin, Zhouchen, Liu, Hong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2020
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary: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.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00897