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  3/2023 - 10
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Video Moment Localization Network Based on Text Multi-semantic Clues Guidance

WU, G. See more information about WU, G. on SCOPUS See more information about WU, G. on IEEExplore See more information about WU, G. on Web of Science, XU, T. See more information about XU, T. on SCOPUS See more information about XU, T. on SCOPUS See more information about XU, T. on Web of Science
 
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Download PDF pdficon (1,730 KB) | Citation | Downloads: 602 | Views: 1,010

Author keywords
information retrieval, machine learning, computer vision, natural language processing, pattern matching

References keywords
vision(24), video(16), cvpr(16), temporal(14), moment(14), localization(14), recognition(12), language(12), iccv(12), videos(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-08-31
Volume 23, Issue 3, Year 2023, On page(s): 85 - 92
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.03010
Web of Science Accession Number: 001062641900010
SCOPUS ID: 85172352256

Abstract
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With the rapid development of the Internet and information technology, people are able to create multimedia data such as pictures or videos anytime and anywhere. Efficient multimedia processing tools are needed for the vast video data. The video moment localization task aims to locate the video moment which best matches the query in the untrimmed video. Existing text-guided methods only consider single-scale text features, which cannot fully represent the semantic features of text, and also do not consider the masking of crucial information in the video by text information when using text to guide the extraction of video features. To solve the above problems, we propose a video moment localization network based on text multi-semantic clues guidance. Specifically, we first design a text encoder based on fusion gate to better capture the semantic information in the text through multi-semantic clues composed of word embedding, local features and global features. Then text guidance module guides the extraction of video features by text semantic features to highlight the video features related to text semantics. Experimental results on two datasets, Charades-STA and ActivityNet Captions, show that our approach provides significant improvements over state-of-the-art methods.


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References Weight

Web of Science® Citations for all references: 64,132 TCR
SCOPUS® Citations for all references: 168,984 TCR

Web of Science® Average Citations per reference: 1,283 ACR
SCOPUS® Average Citations per reference: 3,380 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-11-08 21:17 in 305 seconds.




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