一项关于视频时间接地与多模式大语言模型的调查
IEEE transactions on pattern analysis and machine intelligence
|September 29, 2025
概括
多模式大语言模型 (MLLMs) 正在推进视频时间接地 (VTG) 以更好地理解视频. 本调查回顾了VTG-MLLMs,涵盖了他们的角色,培训和特征处理.
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:
- 视频时间接地 (VTG) 对于细粒度视频理解至关重要.
- 多模式大语言模型 (MLLMs) 显示出卓越的理解和推理能力,推动了VTG的进步.
- 现有的调查缺乏关注VTG-MLLMs的具体进展和应用.
研究的目的:
- 系统地审查和分类关于VTG-MLLMs的当前研究.
- 提供该领域的全面概述,解决现有文献中的差距.
- 分析VTG-MLLMs中的功能角色,训练范式和特征处理技术.
主要方法:
- 使用一个三维分类:MLLM功能角色,训练范式和视频功能处理.
- 分析建筑意义,时间推理策略和时空表示效率.
- 讨论基准数据集,评估协议和经验发现.
主要成果:
- 在性能和通用化方面,VTG-MLLMs超过了传统的微调方法 (零射击,多任务,多域).
- 许多MLLM的架构角色,培训策略和特征处理都对VTG的有效性产生了重大影响.
- 目前的研究表明,MLLM驱动的VTG的能力和潜力越来越大.
结论:
- VTG-MLLMs代表了对细粒度视频理解的重大飞跃.
- 需要进一步的研究来解决现有的局限性,并探索有希望的未来方向.
- 这项调查为了解和推进VTG-MLLM研究提供了基础框架.
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