Detecting Errors in Automatic Image Captioning by Deep Learning;

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Date

2021

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Publisher

Institute of Electrical and Electronics Engineers Inc.

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Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

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Abstract

Automatic tagging of images is an important researcli topic in tlie field of image processing. Anotlier area similar to this is the automatic generation of picture captions. In this study, a deep learning model that automatically tags the pictures is used to detect errors in image captions. As a result of the initial experiments, it is observed that the proposed system can find up to 80% of the errors in the image captions. © 2021 IEEE

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Keywords

Captioning, Deep learning, Deep neural networks, Error detection, Image processing, Tagging

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0

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Source

Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 176826

Volume

Issue

Start Page

46

End Page

49

URI

https://doi.org/10.1109/UBMK52708.2021.9558968
 https://hdl.handle.net/20.500.14411/4039

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Scopus