This paper presents a framework to recognize temporal compositions of atomic actions in videos. Specifically, we propose to express temporal compositions of actions as semantic regular expressions and derive an inference framework using probabilistic automata to recognize complex actions as satisfying these expressions on the input video features. Our approach is different from existing works that either predict long-range complex activities as unordered sets of atomic actions, or retrieve videos using natural language sentences. Instead, the proposed approach allows recognizing complex fine-grained activities using only pretrained action classifiers, without requiring any additional data, annotations or neural network training. To evaluate the potential of our approach, we provide experiments on synthetic datasets and challenging real action recognition datasets, such as MultiTHUMOS and Charades. We conclude that the proposed approach can extend state-of-the-art primitive action classifiers to vastly more complex activities without large performance degradation.

该论文提出了一种框架，用于在视频中识别原子动作的时间组成。通过将行动的时间组成表示为语义正则表达式，并使用概率自动机推导出一个推理框架来识别复杂动作，以满足这些所提供的正则表达式在输入视频特征上。该方法允许仅使用预训练操作分类器来识别复杂的细粒度活动，而无需任何其他数据、注释或神经网络训练，以 vastly more complex activities without large performance degradation扩展最先进的原始动作分类器，这一点在合成数据集和实验中获得了充分的验证。

使用概率自动机推断行为的时间组合