Introduction to the Special Issue on AI-Generated Content for Multimedia

被引：1

作者：

Li, Shengxi ^{[1
]}

Li, Xuelong ^{[2
]}

Chiariglione, Leonardo ^{[3
]}

Luo, Jiebo ^{[4
]}

Wang, Wenwu ^{[5
]}

Yang, Zhengyuan ^{[6
]}

Mandic, Danilo ^{[7
]}

Fujita, Hamido ^{[8
]}

机构：

[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

[2] Northwestern Polytech Univ, Sch Artificial Intelligence OPt & Elect, Xian 710072, Peoples R China

[3] CEDEOnet, I-10040 Villar Dora, Italy

[4] Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA

[5] Univ Surrey, Dept Elect & Elect Engn, Guildford GU2 7XH, England

[6] Microsoft, Redmond, WA 98052 USA

[7] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England

[8] Iwate Prefectural Univ, Dept Software & Informat Sci, Takizawa, Iwate 0200611, Japan

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY | 2024年 / 34卷 / 08期

关键词：

Special issues and sections; Artificial intelligence; Content-based retrieval; Multimedia communication;

D O I：

10.1109/TCSVT.2024.3427488

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Our world is becoming rapidly dependent on data of increasing complexity, diversity, and volume which calls for robust and powerful tools to process such big data. Probabilistic generative models fulfill this goal by learning latent characteristic data relations, especially for the recent emergence of large-scale deep generative models that are able to create realistic content, namely, artificial intelligence-generated content (AIGC). The applications of AIGC span across various domains, and witness rich potential in multimedia content creation, including dialog generation, text-to-speech conversion, image/video generation, and cross-modal content generation.

引用

页码：6809 / 6813

页数：5

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