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通过行为引导微调,将视频模型与人类的社会判断对齐.

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现代人工智能视频模型很难像人类一样理解社会线索. 与人类相似性数据的微调显著改善了他们的社会感知和属性编码.

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科学领域:

  • 人工智能的人工智能
  • 认知科学 认知科学
  • 计算机视觉 计算机视觉

背景情况:

  • 人类在视觉上下文中直观地处理复杂的社会信号.
  • 目前的人工智能模型编码类似人类的社会相似性的能力尚未得到充分理解.
  • 人工智能的视觉处理和人类的社会感知之间存在差距.

研究的目的:

  • 调查AI模型是否能够捕捉社交视频中人类感知到的相似性.
  • 利用行为数据开发方法,将人类社会相似性结构灌输到AI模型中.
  • 解决语言模型在社会相似性任务中优于视频模型的模式差距.

主要方法:

  • 在社交互动视频中创建了超过49000个人类相似性判断的基准.
  • 引入了一种新的混合三重RSA目标,具有低级调整 (LoRA) 来进行微调.
  • 微调了一个使用人类判断的TimeSformer视频模型,以与人类相似性对齐对位距离.

主要成果:

  • 发现一种模式差距:语言模型嵌入更好地匹配人类相似性,而不是视频模型.
  • 精心调整的视频模型显示,对持有数据的人类感知有显著的改进.
  • 微调增强了社会情感属性的编码,如亲密性,价值,主导性和沟通.

结论:

  • 预先训练的视频模型在社交识别能力方面表现出缺陷.
  • 由行为引导的微调有效地塑造了视频表示,使其与人类的社会感知保持一致.
  • 这种方法弥合了人工智能和人类对视频中社会动态的理解之间的差距.