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StructureNet: Deep Context Attention Learning for Structural Component Recognition

Kaothalkar, A; Mandal, B; Puhan, NB

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Authors

A Kaothalkar

NB Puhan



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.

Conference Name 17th International Conference on Computer Vision Theory and Applications
Conference Location Virtual
Start Date Feb 6, 2022
End Date Feb 8, 2022
Acceptance Date Feb 6, 2022
Publication Date Feb 8, 2022
Publicly Available Date Mar 29, 2024
Series Title 17th International Conference on Computer Vision Theory and Applications
ISBN 978-989-758-555-5
DOI https://doi.org/10.5220/0010872800003124
Keywords Class Contexts, Context Attention, Semantic Segmentation, Structural Component Recognition.
Publisher URL https://www.scitepress.org/Link.aspx?doi=10.5220/0010872800003124

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