论文标题
视频动作模型的时间相关性分析
Temporal Relevance Analysis for Video Action Models
论文作者
论文摘要
在本文中，我们对动作识别的时间建模进行了深入的分析，这是文献中重要但毫无疑问的问题。我们首先提出了一种新方法，以量化基于CNN的动作模型捕获的帧之间的时间关系，该帧基于层的相关性传播。然后，我们进行全面的实验和深入分析，以更好地了解时间建模如何受到数据集，网络体系结构和输入框架等各种因素的影响。有了这一点，我们进一步研究了一些重要的行动识别问题，这些问题导致了有趣的发现。我们的分析表明，时间相关性和模型性能之间没有很强的相关性。动作模型倾向于捕获本地时间信息，但长期依赖性较少。我们的代码和模型将公开使用。
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models based on layer-wise relevance propagation. We then conduct comprehensive experiments and in-depth analysis to provide a better understanding of how temporal modeling is affected by various factors such as dataset, network architecture, and input frames. With this, we further study some important questions for action recognition that lead to interesting findings. Our analysis shows that there is no strong correlation between temporal relevance and model performance; and action models tend to capture local temporal information, but less long-range dependencies. Our codes and models will be publicly available.
