Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.5220/0010872800003124 | PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5 |
Keywords | DocType | ISSN |
Class Contexts, Context Attention, Semantic Segmentation, Structural Component Recognition | Conference | 2184-4321 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akash Kaothalkar | 1 | 0 | 0.34 |
| Bappaditya Mandal | 2 | 0 | 0.34 |
| Niladri B. Puhan | 3 | 0 | 0.34 |