Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications

Changhoon Jeong, Sung-Eun Jang, Sanghyuck Na and Juntae Kim
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Changhoon Jeong: Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea
Sung-Eun Jang: Department of Intelligence, Dongguk University, Seoul 04620, Korea
Sanghyuck Na: Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea
Juntae Kim: Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea

Data, 2019, vol. 4, issue 4, 1-9

Abstract: 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.

Keywords: social network service; Korean tourist spot; deep learning; multi-modal learning; Korean text (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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