Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications

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...

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Bibliographic Details
Published in:Data (Basel) 2019-12, Vol.4 (4), p.139
Main Authors: Jeong, Changhoon, Jang, Sung-Eun, Kim, Juntae
Format: Article
Language:English
Subjects:
Classification
Data collection
Datasets
Deep learning
Image classification
korean text
korean tourist spot
Machine learning
multi-modal learning
Personal information
Restaurants
social network service
Social networks
Social research
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Summary: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.
ISSN:2306-5729
2306-5729
DOI:10.3390/data4040139