计算机科学
对话框
多媒体
生成模型
生成语法
人工智能
内容(测量理论)
过程(计算)
情态动词
大数据
证人
万维网
数据挖掘
数学分析
数学
化学
高分子化学
程序设计语言
操作系统
作者
Shengxi Li,Xuelong Li,Leonardo Chiariglione,Jiebo Luo,Wenwu Wang,Zhengyuan Yang,Danilo P. Mandic,Hamido Fujita
标识
DOI:10.1109/tcsvt.2024.3427488
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.
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