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Authors: Hyunwoo Kim 1 ; Huaiyu Li 2 and Seok-Cheol Kee 3

Affiliations: 1 Beijing Advanced Innovation Center for Intelligent Robotics and Systems, Beijing Institute of Technology, Beijing and China ; 2 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing and China ; 3 Smart Car Research Center, Chungbuk National University, Cheongju and South Korea

Keyword(s): Convolutional Network, Joint Training, Global Context, Semantic Scene Segmentation.

Abstract: The state-of-the-art semantic segmentation tasks can be achieved by the variants of the fully convolutional neural networks (FCNs), which consist of the feature encoding and the deconvolution. However, they struggle with missing or inconsistent labels. To alleviate these problems, we utilize the image-level multi-class encoding as the global contextual information. By incorporating object classification into the objective function, we can reduce incorrect pixel-level segmentation. Experimental results show that our algorithm can achieve better performance than other methods on the same level training data volume.

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Paper citation in several formats:
Kim, H.; Li, H. and Kee, S. (2019). GCCNet: Global Context Constraint Network for Semantic Segmentation. In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-374-2; ISSN 2184-495X, SciTePress, pages 380-387. DOI: 10.5220/0007705703800387

@conference{vehits19,
author={Hyunwoo Kim. and Huaiyu Li. and Seok{-}Cheol Kee.},
title={GCCNet: Global Context Constraint Network for Semantic Segmentation},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2019},
pages={380-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007705703800387},
isbn={978-989-758-374-2},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - GCCNet: Global Context Constraint Network for Semantic Segmentation
SN - 978-989-758-374-2
IS - 2184-495X
AU - Kim, H.
AU - Li, H.
AU - Kee, S.
PY - 2019
SP - 380
EP - 387
DO - 10.5220/0007705703800387
PB - SciTePress

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