CMFF_VS: A Video Summarization Extraction Model based on Cross-modal Feature Fusion.
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| Title: | CMFF_VS: A Video Summarization Extraction Model based on Cross-modal Feature Fusion. |
|---|---|
| Authors: | Xin, Chaoqun1 (AUTHOR) xinchaoqun@nefu.edu.cn, Wang, Mingyang1 (AUTHOR) wangmingyang@nefu.edu.cn, Zhao, Xianhao1 (AUTHOR) soramiaktio@nefu.edu.cn |
| Source: | Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Dec2025, Vol. 50 Issue 23, p19153-19167. 15p. |
| Subjects: | Video summarization, Feature extraction, Machine learning |
| Abstract: | Video summarization aims to present the most relevant and important information in the video stream in the form of a summary. Most existing researches focus on the selection process of keyframes, determining the importance of video frames by obtaining dependency information between them. However, these works overlook the feature extraction process of video frames. In fact, rich and reliable video frame features are an important basis for determining whether video frames can be selected correctly. This article proposes a new video summarization extraction model based on cross-modal feature fusion (CMFF_VS). CMFF_VS model utilizes the mutual enhancement of video modality and text modality to extract richer semantic information of video frames, thereby providing necessary features for the subsequent video frame selection process. To solve the alignment problem between semantic information of two modalities, CMFF_VS introduces a cross-modal attention mechanism, which utilizes the semantic correlation of modalities to achieve cross-modal semantic fusion. At the same time, CMFF_VS introduces the ASPP module to extract and fuse multi-scale semantic features of individual modalities, enriching the capture of advanced semantic information for each modality. The experimental results show that compared with the state-of-the-art unimodal and multimodal video summarization models, CMFF-VS achieves the best performance, indicating that the cross-modal deep feature extraction and fusion strategy proposed in CMFF-VS is reasonable and effective. [ABSTRACT FROM AUTHOR] |
| Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189532017 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CMFF_VS: A Video Summarization Extraction Model based on Cross-modal Feature Fusion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xin%2C+Chaoqun%22">Xin, Chaoqun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xinchaoqun@nefu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Mingyang%22">Wang, Mingyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangmingyang@nefu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xianhao%22">Zhao, Xianhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> soramiaktio@nefu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Arabian+Journal+for+Science+%26+Engineering+%28Springer+Science+%26+Business+Media+B%2EV%2E+%29%22">Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )</searchLink>. Dec2025, Vol. 50 Issue 23, p19153-19167. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Video+summarization%22">Video summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Video summarization aims to present the most relevant and important information in the video stream in the form of a summary. Most existing researches focus on the selection process of keyframes, determining the importance of video frames by obtaining dependency information between them. However, these works overlook the feature extraction process of video frames. In fact, rich and reliable video frame features are an important basis for determining whether video frames can be selected correctly. This article proposes a new video summarization extraction model based on cross-modal feature fusion (CMFF_VS). CMFF_VS model utilizes the mutual enhancement of video modality and text modality to extract richer semantic information of video frames, thereby providing necessary features for the subsequent video frame selection process. To solve the alignment problem between semantic information of two modalities, CMFF_VS introduces a cross-modal attention mechanism, which utilizes the semantic correlation of modalities to achieve cross-modal semantic fusion. At the same time, CMFF_VS introduces the ASPP module to extract and fuse multi-scale semantic features of individual modalities, enriching the capture of advanced semantic information for each modality. The experimental results show that compared with the state-of-the-art unimodal and multimodal video summarization models, CMFF-VS achieves the best performance, indicating that the cross-modal deep feature extraction and fusion strategy proposed in CMFF-VS is reasonable and effective. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13369-025-10133-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 19153 Subjects: – SubjectFull: Video summarization Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: CMFF_VS: A Video Summarization Extraction Model based on Cross-modal Feature Fusion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xin, Chaoqun – PersonEntity: Name: NameFull: Wang, Mingyang – PersonEntity: Name: NameFull: Zhao, Xianhao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2193567X Numbering: – Type: volume Value: 50 – Type: issue Value: 23 Titles: – TitleFull: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) Type: main |
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