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.
Authors
- Hamido Fujita (ORCID: https://orcid.org/0000-0001-5256-210X)
- Zhengyuan Yang (ORCID: https://orcid.org/0000-0002-5808-0889)
- Jiebo Luo (ORCID: https://orcid.org/0000-0002-4516-9729)
- Leonardo Chiariglione (ORCID: https://orcid.org/0000-0001-7234-3967)
- Wenwu Wang (ORCID: https://orcid.org/0000-0002-8393-5703)
- Xuelong Li (ORCID: https://orcid.org/0000-0002-0019-4197)
- Shengxi Li (ORCID: https://orcid.org/0000-0003-4979-9290)
- Danilo P. Mandic (ORCID: https://orcid.org/0000-0001-8432-3963)
Institutions
- Microsoft (United States) (US)
- Northwestern Polytechnical University (CN)
- University of Surrey (GB)
- CEDEO (Italy) (IT)
- Imperial College London (GB)
- University of Rochester (US)
- Iwate Prefectural University (JP)
- Beihang University (CN)
Publication Details
- Journal
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1109/tcsvt.2024.3427488
- Citations
- 4
- Primary Topic
- Digital Humanities and Scholarship
- Type
- article
- Field-Weighted Citation Impact
- 15.63