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动力器++:通过精细的长视频推理来推进视频大语言模型
IEEE transactions on pattern analysis and machine intelligence
|January 20, 2026
概括
一个新的视频LLMMMomentor增强了视频中微细的时间理解和本地化. 它的改进版本,Momentor++,通过空间时间令牌巩固有效处理复杂的,扩展的视频.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 大型语言模型 (LLM) 在文本任务中表现出色,但在视频的时间复杂性方面扎.
- 现有的视频LLM缺乏精细的时间理解和高效的分段本地化.
- 解决这些局限性对于促进视频理解至关重要.
研究的目的:
- 介绍Momentor,一个用于精细的时间理解和定位的视频LLM.
- 开发Moment-10M,这是一个大型数据集,用于训练分段级视频指导任务.
- 提高视频LLM中的计算效率和细节保存.
主要方法:
- 提出了Momentor,这是一个用于详细时间视频分析的Video-LLM架构.
- 使用自动数据生成引擎创建了Moment-10M数据集.
- 引入了空间时间令牌整合 (STTC) 以实现无参数令牌合并.
主要成果:
- 在精细的时间理解和定位任务中,Momentor表现出强的表现.
- Moment-10M数据集促进了视频LLM的有效培训.
- 使用STTC的Momentor++显著提高了扩展视频的计算效率.
结论:
- 动力器为视频提供了强大的细粒度时间理解能力.
- Momentor++提供了复杂,长视频的高效处理,并增强了时间上下文.
- 开发的方法推动了视频LLM领域的发展,用于详细的视频分析.
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