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短视频中文标题生成:数据集,度量和算法
IEEE transactions on pattern analysis and machine intelligence
|February 14, 2024
概括
本研究介绍了CREATE,这是一个大型的中国视频标题和标题数据集,解决了缺少基准的问题. 它还介绍了评估指标ACTEr和视频分析和生成的多模式模型ALWIG.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 现有的视频标题方法缺乏人类的兴趣和吸引力.
- 视频标题生成 (标题) 缺乏标准化的基准来评估有吸引力的标题.
- 需要全面的资源来推进中国短视频分析.
研究的目的:
- 介绍CREATE,这是中国首个用于视频检索和标题生成的大规模数据集.
- 提出ACTEr,一种用于评估生成视频标题吸引力和相关性的新指标.
- 介绍ALWIG,一个用于同时视频标题,标题和检索的多模式模型.
主要方法:
- 开发了CREATE数据集,包含210K标记,3M/10M预训数据,涵盖51个类别和广泛的注释.
- 引入了ACTEr (基于吸引力共识的标题评估) 度量,使用语义相关性和共识权重.
- 拟议的ALWIG (多模式ALignment WIth Generation) 模型具有标签驱动的对齐和基于GPT的生成.
主要成果:
- CREATE数据集为中国短视频研究提供了坚实的基础.
- ACTEr通过考虑吸引力和相关性客观地评估视频标题质量.
- ALWIG模型展示了作为多模式视频任务的基线的强大性能.
结论:
- 预计CREATE,ACTEr和ALWIG的发布将刺激中国短视频分析和创作的进一步研究.
- 这项工作弥合了客观视频描述和引人入胜的标题生成之间的差距.
- 开发的资源促进了视频标题,标题和检索应用的进步.
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