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Authors: Akash Kaothalkar 1 ; Bappaditya Mandal 2 and Niladri. B. Puhan 1

Affiliations: 1 Indian Institute of Technology Bhubaneswar, India ; 2 Keele University, Newcastle-under-Lyme, U.K.

Keyword(s): Class Contexts, Context Attention, Semantic Segmentation, Structural Component Recognition.

Abstract: Structural component recognition using images is a very challenging task due to the appearance of large components and their long continuation, existing jointly with very small components, the latter are often outcasted/missed by the existing methodologies. In this work, various categories of the bridge components are exploited at the contextual level information encoding across spatial as well as channel dimensions. Tensor decomposition is used to design a context attention framework that acquires crucial information across various dimensions by fusing the class contexts and 3-D attention map. Experimental results on benchmarking bridge component classification dataset show that our proposed architecture attains superior results as compared to the current state-of-the-art methodologies.

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Paper citation in several formats:
Kaothalkar, A.; Mandal, B. and Puhan, N. (2022). StructureNet: Deep Context Attention Learning for Structural Component Recognition. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 567-573. DOI: 10.5220/0010872800003124

@conference{visapp22,
author={Akash Kaothalkar. and Bappaditya Mandal. and Niladri. B. Puhan.},
title={StructureNet: Deep Context Attention Learning for Structural Component Recognition},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={567-573},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010872800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - StructureNet: Deep Context Attention Learning for Structural Component Recognition
SN - 978-989-758-555-5
IS - 2184-4321
AU - Kaothalkar, A.
AU - Mandal, B.
AU - Puhan, N.
PY - 2022
SP - 567
EP - 573
DO - 10.5220/0010872800003124
PB - SciTePress

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