Arsan, Taner

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A., Taner
Taner Arsan
ARSAN, Taner
Arsan,Taner
Arsan, TANER
A.,Taner
Taner ARSAN
Arsan, Taner
Taner, Arsan
ARSAN, TANER
Arsan, T.
T. Arsan
TANER ARSAN
Arsan,T.
Arsan T.
Job Title
Doç. Dr.
Email Address
arsan@khas.edu.tr
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Computer Engineering
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Website
ORCID ID
0000-0002-4453-3218
Scopus Author ID
6506505859
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Google Scholar ID
WoS Researcher ID
AAB-2736-2019
Scholarly Output

66

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20

Citation Count

140

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Author: Alsan, H.F.Subject: Convolutional networks

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    Citation - Scopus: 4
    Multimodal Retrieval With Contrastive Pretraining
    (Institute of Electrical and Electronics Engineers Inc., 2021) Alsan, H.F.; Arsan, Taner; Yildiz, E.; Safdil, E.B.; Arslan, F.; Arsan, T.
    In this paper, we present multimodal data retrieval aided with contrastive pretraining. Our approach is to pretrain a contrastive network to assist in multimodal retrieval tasks. We work with multimodal data, which has image and caption (text) pairs. We present a dual encoder deep neural network with the image and text encoder to encode multimodal data (images and text) to represent vectors. These representation vectors are used for similarity-based retrieval. Image encoder is a 2D convolutional network, and text encoder is a recurrent neural network (Long-Short Term Memory). MS-COCO 2014 dataset has both images and captions, and it is used for multimodal training with triplet loss. We used a convolutional Siamese network to compute the similarities between images before the dual encoder training (contrastive pretraining). The advantage is that Siamese networks can aid the retrieval, and we seek to show if Siamese networks can be used in practice. Finally, we investigated the performance of Siamese assisted retrieval with BLEU score metric. We conclude that Siamese can help with image-to-text retrieval tasks. © 2021 IEEE.
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    Conference Object
    Citation - Scopus: 0
    Multitype Learning Via Multimodal Data Embedding
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yildiz, E.; Arsan, Taner; Safdil, E.B.; Arslan, F.; Alsan, H.F.; Arsan, T.
    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.