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http://dx.doi.org/10.9717/kmms.2020.24.1.001

Multi-Path Feature Fusion Module for Semantic Segmentation  

Park, Sangyong (Dept. of Electrical and Computer Engineering, Ajou University)
Heo, Yong Seok (Dept. of Electrical and Computer Engineering, Ajou University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.1, 2021 , pp. 1-12 More about this Journal
Abstract
In this paper, we present a new architecture for semantic segmentation. Semantic segmentation aims at a pixel-wise classification which is important to fully understand images. Previous semantic segmentation networks use features of multi-layers in the encoder to predict final results. However, they do not contain various receptive fields in the multi-layers features, which easily lead to inaccurate results for boundaries between different classes and small objects. To solve this problem, we propose a multi-path feature fusion module that allows for features of each layers to contain various receptive fields by use of a set of dilated convolutions with different dilatation rates. Various experiments demonstrate that our method outperforms previous methods in terms of mean intersection over unit (mIoU).
Keywords
Semantic Segmentation; Multi-path Features Fusion Module;
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