IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v4y2019i4p139-d275705.html
   My bibliography  Save this article

Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications

Author

Listed:
  • 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)

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.

Suggested Citation

  • Changhoon Jeong & Sung-Eun Jang & Sanghyuck Na & Juntae Kim, 2019. "Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications," Data, MDPI, vol. 4(4), pages 1-9, October.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:4:p:139-:d:275705
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/4/4/139/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/4/4/139/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:4:y:2019:i:4:p:139-:d:275705. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.