概念意识的视频标题:用有效的预先信息来描述视频
概括
概念意识的视频标题框架 (CARE) 通过精确检测概念并将其用于更好的标题生成来提高准确性. 这种方法增强了先前的信息,导致更合理的视频描述.
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:
- 视频标题旨在为视频内容生成文字描述.
- 现有的方法在不准确的概念检测和不足利用方面扎,导致标题质量不足.
- 作为有意义的词汇,概念可以弥合视觉和文本信息之间的差距.
研究的目的:
- 提出一个新的框架,概念意识的视频标题 (CARE),用于更合理的视频标题生成.
- 解决当前视频标题模型中概念检测精度和利用的局限性.
- 增强提供给标题生成模型的先前信息.
主要方法:
- 为了精确的概念识别,CARE采用了一个编码器-解码器结构,具有多式模式驱动的概念检测 (MCD).
- 全球-本地语义指导 (G-LSG) 用于有效地利用检测到的概念来生成标题.
- 从对比视觉语言预训练模型 (CLIP) 转移知识是CARE视觉理解和检索能力的核心.
主要成果:
- 与强大的基于CLIP的视频标题基线相比,CARE的表现优越.
- 该框架在包括MSVD,MSR-VTT和VATEX在内的基准数据集上取得了最先进的结果.
- CARE 显示了多功能性,与不同的编码器-解码器架构表现良好.
结论:
- 拟议的CARE框架通过增强概念检测和利用,有效地改善视频标题.
- 利用CLIP的知识转移显著提高了该模型的性能.
- 对于生成更准确和更相关的视频标题,CARE提供了一个有希望的方向.
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