Multitype Learning Via Multimodal Data Embedding
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Date
2021
Authors
Yildiz, E.
Safdil, E.B.
Arslan, F.
Alsan, H.F.
Arsan, T.
Journal Title
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Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Open Access Color
Green Open Access
No
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No
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Abstract
This paper creates a multimodal retrieval system for image and text data in a multi-type learning approach that enables text-to-image, image-to-text, text-to-text, and image-to-image retrievals. As a practical solution, a mobile application is developed in which the users can upload their images to search a description sentence for the images. The user system is created on the application, which is done with React Native, and crucial features like e-mail authentication and reset password options are added to the application. An essential database system is designed with PostgreSQL to store user information and search for the user. The multimodal embedding study is worked, and the model that recognizes multitype retrievals is formed. The image-to-text retrieval model, which is our application's idea, is applied to the mobile application. © 2021 IEEE.
Description
5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 --21 October 2021 through 23 October 2021 -- --174473
Keywords
Convolutional Networks, Cross-Modal Learning, Deep Learning, Long-Short Term Memory (LSTM), Mobile Application, Multimodal Retrieval, React Native, Authentication, Convolutional neural networks, Embeddings, Information retrieval, Mobile computing, Search engines, Convolutional networks, Cross-modal, Cross-modal learning, Deep learning, Long-short term memory, Mobile applications, Multi-modal, Multimodal retrieval, Multitype, React native, Long short-term memory, Cross-modal learning, Authentication, Mobile computing, Multimodal Retrieval, Convolutional Networks, Long-Short Term Memory (LSTM), Mobile Application, Long-short term memory, Deep learning, Multitype, Cross-Modal Learning, Embeddings, Multimodal retrieval, React native, Deep Learning, Mobile applications, React Native, Cross-modal, Multi-modal, Search engines, Long short-term memory, Information retrieval, Convolutional neural networks, Convolutional networks
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Source
ISMSIT 2021 - 5th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
Volume
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Start Page
457
End Page
461
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Scopus : 0
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7
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