Enhanced Video Caption Generation Based on Multimodal Features

Xuefei Huang, Wei Ke, Hao Sheng
{"title":"Enhanced Video Caption Generation Based on Multimodal Features","authors":"Xuefei Huang, Wei Ke, Hao Sheng","doi":"10.1109/UV56588.2022.10185501","DOIUrl":null,"url":null,"abstract":"Video caption is the automatically generated of abstract expressions for the content contained in videos. It involves two important fields — computer vision and natural language processing, and has become a considerable research topic in smart life. Deep learning has successfully contributed to this task with good results. As we know, video contains various modals of information, yet most of the existing solutions start from the visual perspective of video, while ignoring the equally important audio modal information. Therefore, how to benefit from additional forms of cues other than visual information is a huge challenge. In this work, we propose a video caption generation method that fuses multimodal features in videos, and adds attention mechanism to improve the quality of generated description sentences. The experimental results demonstrate that the method is well validated on the MSR-VTT dataset.","PeriodicalId":211011,"journal":{"name":"2022 6th International Conference on Universal Village (UV)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 6th International Conference on Universal Village (UV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UV56588.2022.10185501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Video caption is the automatically generated of abstract expressions for the content contained in videos. It involves two important fields — computer vision and natural language processing, and has become a considerable research topic in smart life. Deep learning has successfully contributed to this task with good results. As we know, video contains various modals of information, yet most of the existing solutions start from the visual perspective of video, while ignoring the equally important audio modal information. Therefore, how to benefit from additional forms of cues other than visual information is a huge challenge. In this work, we propose a video caption generation method that fuses multimodal features in videos, and adds attention mechanism to improve the quality of generated description sentences. The experimental results demonstrate that the method is well validated on the MSR-VTT dataset.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于多模态特征的增强视频字幕生成
视频字幕是对视频内容自动生成的抽象表达。它涉及计算机视觉和自然语言处理两个重要领域,已成为智能生活中一个相当重要的研究课题。深度学习已经成功地为这项任务做出了贡献,并取得了良好的效果。众所周知,视频包含了多种多样的信息模态,但现有的解决方案大多是从视频的视觉角度出发,而忽略了同样重要的音频模态信息。因此,如何从视觉信息以外的其他形式的线索中获益是一个巨大的挑战。在这项工作中,我们提出了一种融合视频中多模态特征的视频字幕生成方法,并增加了注意机制来提高生成的描述句的质量。实验结果表明,该方法在MSR-VTT数据集上得到了很好的验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Generative Cooperative Network for Person Image Generation Image Caption Enhancement with GRIT, Portable ResNet and BART Context-Tuning Dynamical Simulation Study of Hybrid Solar-Fossil Fuel Thermochemical Storage and Electricity, Heat and Cold Generation System Bag of Tricks for “Vision Meet Alage” Object Detection Challenge Density Functional Theory Study of Adding Ionic Liquid to Aqueous Ammonia System
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1