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Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery

Gaus, Y. F. A.; Bhowmik, N.; Isaac-Medina, B. K. S.; Breckon, T. P.

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Citation

Gaus, Y. F. A., Bhowmik, N., Isaac-Medina, B. K. S., & Breckon, T. P. (2024, June). Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA

Presentation Conference Type Conference Paper (published)
Conference Name 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Start Date Jun 17, 2024
End Date Jun 18, 2024
Acceptance Date Apr 1, 2024
Online Publication Date Sep 27, 2024
Publication Date Sep 27, 2024
Deposit Date Apr 23, 2024
Publicly Available Date Oct 9, 2024
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 3142-3152
Series ISSN 2160-7508
Book Title 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
DOI https://doi.org/10.1109/CVPRW63382.2024.00320
Keywords x-ray, thermal, infrared, foundational model, SAM, segmentation, semantic segmentation, instance segmantation
Public URL https://durham-repository.worktribe.com/output/2393936
Related Public URLs https://breckon.org/tob...apers/gaus24segment.pdf

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