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Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications

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Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea
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Department of Intelligence, Dongguk University, Seoul 04620, Korea
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Author to whom correspondence should be addressed.
Data 2019, 4(4), 139; https://doi.org/10.3390/data4040139
Received: 6 September 2019 / Revised: 2 October 2019 / Accepted: 8 October 2019 / Published: 12 October 2019
Recently, deep learning-based methods for solving multi-modal tasks such as image captioning, multi-modal classification, and cross-modal retrieval have attracted much attention. To apply deep learning for such tasks, large amounts of data are needed for training. However, although there are several Korean single-modal datasets, there are not enough Korean multi-modal datasets. In this paper, we introduce a KTS (Korean tourist spot) dataset for Korean multi-modal deep-learning research. The KTS dataset has four modalities (image, text, hashtags, and likes) and consists of 10 classes related to Korean tourist spots. All data were extracted from Instagram and preprocessed. We performed two experiments, image classification and image captioning with the dataset, and they showed appropriate results. We hope that many researchers will use this dataset for multi-modal deep-learning research. View Full-Text
Keywords: social network service; Korean tourist spot; deep learning; multi-modal learning; Korean text social network service; Korean tourist spot; deep learning; multi-modal learning; Korean text
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Jeong, C.; Jang, S.-E.; Na, S.; Kim, J. Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications. Data 2019, 4, 139.

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