Cap4Video++:通过辅助字幕增强视频理解
IEEE transactions on pattern analysis and machine intelligence
|September 9, 2024
概括
Cap4Video++通过将视觉语言模型 (VLMs) 和大语言模型 (LLMs) 与用户生成的字幕集成来增强视频理解. 这一框架显著改善了文本视频检索和动作识别任务.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 视频理解和文本对齐是计算机视觉中具有挑战性的任务.
- 视觉语言模型 (VLM) 是有前途的,但经常忽视用户生成的元数据,如标题.
- 大型语言模型 (LLM) 为多式联络理解提供了新的机会.
研究的目的:
- 介绍Cap4Video++,一个利用辅助字幕来丰富视频理解的框架.
- 为了利用VLM和LLM之间的协同作用,进行先进的视频标题和分析.
- 为了提高文本视频检索和视频动作识别的性能.
主要方法:
- 在输入阶段使用语义对抽样来进行对比学习.
- 在中间阶段实施视频字幕交叉模式交互和自适应字幕选择.
- 在输出阶段使用补充标题-文本匹配分支来增强相似性计算.
主要成果:
- 在文本视频检索任务上,Cap4Video++在现有模型上表现出优越的性能.
- 该框架显示,视频动作识别在9个基准中显著改善.
- 实验证实了用于视频理解的自动生成字幕的有效性.
结论:
- Cap4Video++提供了一个通用框架,通过辅助字幕来提高视频理解.
- 在有效的视频-文本对齐中,VLM和LLM的协同作用至关重要.
- 提出的方法显著提高了用于视频分析的元数据的利用率.
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