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Authors: Chuan Wang and Long Chang

Affiliation: Department of Computer Science, National Tsing Hua University and Republic of China

ISBN: 978-989-758-351-3

Keyword(s): Semantic Segmentation, Convolutional Neural Network, Global Convolutional Network, DenseNet, Concatenation, ResNet, FC-DenseNet, and CamVid.

Abstract: Most of the segmentation CNNs (convolutional neural network) based on the ResNet. Recently, Huang et al. introduced a new classification CNN called DenseNet. Then Jégou et al. used a sequence of building blocks for DenseNet to build their semantic segmentation CNN, called FC-DenseNet, and achieved state-of-the-art results on CamVid dataset. In this paper, we implement the design concept of DenseNet into a ResNet-based semantic segmentation CNN called Global Convolutional Network (GCN) and build our own network by switching every identity mapping operation of the decoder network in GCN to a concatenation operation. Our network uses less computational resources than FC-DenseNet to obtain a mean IoU score of 69.34% on CamVid dataset, and surpass the 66.9% obtained in the paper of FC-DenseNet.

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Paper citation in several formats:
Wang, C. and Chang, L. (2019). Semantic Segmentation via Global Convolutional Network and Concatenated Feature Maps.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 292-297. DOI: 10.5220/0007251002920297

@conference{icpram19,
author={Chuan Kai Wang. and Long Wen Chang.},
title={Semantic Segmentation via Global Convolutional Network and Concatenated Feature Maps},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={292-297},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007251002920297},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Semantic Segmentation via Global Convolutional Network and Concatenated Feature Maps
SN - 978-989-758-351-3
AU - Wang, C.
AU - Chang, L.
PY - 2019
SP - 292
EP - 297
DO - 10.5220/0007251002920297

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