空间时空脱的知识补偿器,用于少数射击动作识别
概括
本研究介绍了DiST,这是一种用于Few-Shot Action Recognition (FSAR) 的新型框架. DiST利用大型语言模型将动作名称分解为空间和时间知识,在有限的数据中提高识别准确性.
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:
- 由于新型行动类别的标记数据有限,FSAR面临挑战.
- 现有的方法依赖于粗略的动作名称,为视觉特征学习提供了不足的背景知识.
- 需要更丰富的上下文信息来捕捉复杂的空间和时间行动动态.
研究的目的:
- 提出DiST,一个分解整合框架来增强FSAR.
- 利用从大型语言模型中解的空间和时间知识来学习表达式原型.
- 提高在动作识别中对新型空间和时间概念的理解.
主要方法:
- 动作名称分解为不同的时空属性描述.
- 使用空间/时间知识补偿器 (SKC/TKC) 整合空间和时间知识.
- 以常识知识为指导的对象级和框架级原型的学习.
主要成果:
- 通过整合常识知识,DiST有效地学习多细分化原型.
- SKC集成使用空间知识进行对象级理解的补丁令牌.
- TKC模型使用时间属性来理解框架层次的框架间时间关系.
- 在五个标准的FSAR数据集上,DiST实现了最先进的性能.
结论:
- DiST框架通过利用LLM的分解知识,为FSAR提供了一种新的方法.
- 学习的原型在捕捉细粒度的空间细节和各种时间模式方面提供了透明度.
- DiST显著提升了动作识别系统的功能,只有有限的训练示例.
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