人工智能生成的多媒体内容特刊简介

IF 8.3 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-12 DOI:10.1109/TCSVT.2024.3427488

Shengxi Li;Xuelong Li;Leonardo Chiariglione;Jiebo Luo;Wenwu Wang;Zhengyuan Yang;Danilo Mandic;Hamido Fujita

{"title":"人工智能生成的多媒体内容特刊简介","authors":"Shengxi Li;Xuelong Li;Leonardo Chiariglione;Jiebo Luo;Wenwu Wang;Zhengyuan Yang;Danilo Mandic;Hamido Fujita","doi":"10.1109/TCSVT.2024.3427488","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"34 8","pages":"6809-6813"},"PeriodicalIF":8.3000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634078","citationCount":"0","resultStr":"{\"title\":\"Introduction to the Special Issue on AI-Generated Content for Multimedia\",\"authors\":\"Shengxi Li;Xuelong Li;Leonardo Chiariglione;Jiebo Luo;Wenwu Wang;Zhengyuan Yang;Danilo Mandic;Hamido Fujita\",\"doi\":\"10.1109/TCSVT.2024.3427488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":13082,\"journal\":{\"name\":\"IEEE Transactions on Circuits and Systems for Video Technology\",\"volume\":\"34 8\",\"pages\":\"6809-6813\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634078\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Circuits and Systems for Video Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10634078/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems for Video Technology","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10634078/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

摘要

我们的世界正变得越来越依赖于复杂性、多样性和数量不断增加的数据，这就需要强大而有力的工具来处理这些大数据。概率生成模型通过学习潜在的特征数据关系实现了这一目标，特别是最近出现的大规模深度生成模型，能够创建逼真的内容，即人工智能生成内容（AIGC）。人工智能生成内容（AIGC）的应用横跨多个领域，在多媒体内容创建方面具有巨大潜力，包括对话生成、文本到语音转换、图像/视频生成和跨模态内容生成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Circuits and Systems for Video Technology 工程技术-工程：电子与电气

CiteScore

13.80

自引率

27.40%

发文量

660

审稿时长

5 months

期刊介绍： The IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) is dedicated to covering all aspects of video technologies from a circuits and systems perspective. We encourage submissions of general, theoretical, and application-oriented papers related to image and video acquisition, representation, presentation, and display. Additionally, we welcome contributions in areas such as processing, filtering, and transforms; analysis and synthesis; learning and understanding; compression, transmission, communication, and networking; as well as storage, retrieval, indexing, and search. Furthermore, papers focusing on hardware and software design and implementation are highly valued. Join us in advancing the field of video technology through innovative research and insights.