Kaothalkar, A, Mandal, B ORCID: https://orcid.org/0000-0001-8417-1410 and Puhan, N (2022) StructureNet: Deep Context Attention Learning for Structural Component Recognition. In: 17th International Conference on Computer Vision Theory and Applications.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website.
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Depositing User: Symplectic
Date Deposited: 20 May 2022 07:56
Last Modified: 20 May 2022 07:56
URI: https://eprints.keele.ac.uk/id/eprint/10947

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