IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i9p294-d1230760.html
   My bibliography  Save this article

Advances Techniques in Computer Vision and Multimedia

Author

Listed:
  • Yang Wang

    (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China)

Abstract

Computer vision has experienced significant advancements and great success in areas closely related to human society, which aims to enable computer systems to automatically see, recognize, and understand the visual world by simulating the mechanism of human vision [...]

Suggested Citation

  • Yang Wang, 2023. "Advances Techniques in Computer Vision and Multimedia," Future Internet, MDPI, vol. 15(9), pages 1-2, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:294-:d:1230760
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/9/294/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/9/294/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liewu Cai & Lei Zhu & Hongyan Zhang & Xinghui Zhu, 2022. "DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval," Future Internet, MDPI, vol. 14(2), pages 1-23, January.
    2. Pavel Laptev & Sergey Litovkin & Sergey Davydenko & Anton Konev & Evgeny Kostyuchenko & Alexander Shelupanov, 2022. "Neural Network-Based Price Tag Data Analysis," Future Internet, MDPI, vol. 14(3), pages 1-14, March.
    3. Jiagang Song & Jiayu Song & Xinpan Yuan & Xiao He & Xinghui Zhu, 2022. "Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation," Future Internet, MDPI, vol. 14(2), pages 1-17, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guokun Li & Zhen Wang & Shibo Xu & Chuang Feng & Xiaohan Yang & Nannan Wu & Fuzhen Sun, 2022. "Deep Adversarial Learning Triplet Similarity Preserving Cross-Modal Retrieval Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-16, July.

    More about this item

    Keywords

    n/a;

    Statistics

    Access and download statistics

    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:jftint:v:15:y:2023:i:9:p:294-:d:1230760. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.