Understanding Convolution for Semantic Segmentation

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Authors Panqu Wang, Ye Yuan, Garrison Cottrell, Ding Liu, Xiaodi Hou, Pengfei Chen, Zehua Huang
Journal/Conference Name Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Paper Category
Paper Abstract Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-the-art overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https//github.com/TuSimple/TuSimple-DUC .
Date of publication 2017
Code Programming Language Multiple
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