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Guest Editorial Introduction to the Special Section on Video and Language

DC Field Value Language
dc.contributor.authorMei, Tao-
dc.contributor.authorCorso, Jason J.-
dc.contributor.authorKim, Gun Hee-
dc.contributor.authorLuo, Jiebo-
dc.contributor.authorShen, Chunhua-
dc.contributor.authorZhang, Hanwang-
dc.date.accessioned2022-06-24T07:03:41Z-
dc.date.available2022-06-24T07:03:41Z-
dc.date.created2022-05-09-
dc.date.issued2022-01-
dc.identifier.citationIEEE Transactions on Circuits and Systems for Video Technology, Vol.32 No.1, pp.1-4-
dc.identifier.issn1051-8215-
dc.identifier.urihttps://hdl.handle.net/10371/184005-
dc.description.abstractComputer Vision (CV) and Natural Language Processing (NLP) are two most fundamental disciplines under a broad area of artificial intelligence (AI). CV is regarded as a field of research that explores the techniques to teach computers to see and understand digital content such as images and videos. NLP is a branch of linguistics that enables computers to process, interpret, and even generate human language. With the rise and development of deep learning over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the limits and improve the state-of-the-art of both vision and language modeling. An interesting observation is that the research in the two areas starts to interact, with a significant growth in both the volume of publications and extensive applications. Meanwhile, many previous experiences have shown that this can naturally build up the circle of human intelligence.-
dc.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.titleGuest Editorial Introduction to the Special Section on Video and Language-
dc.typeArticle-
dc.identifier.doi10.1109/TCSVT.2021.3137430-
dc.citation.journaltitleIEEE Transactions on Circuits and Systems for Video Technology-
dc.identifier.wosid000742183600002-
dc.identifier.scopusid2-s2.0-85123753762-
dc.citation.endpage4-
dc.citation.number1-
dc.citation.startpage1-
dc.citation.volume32-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorKim, Gun Hee-
dc.type.docTypeEditorial-
dc.description.journalClass1-
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