Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. Recent methods attempt to capture this structure and learn action representations with convolutional neural networks. Such representations, however, are typically learned at the level of a few video frames failing to model actions at their full temporal extent. In this work we learn video representations using neural networks with long-term temporal convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal extents improve the accuracy of action recognition. We also study the impact of different low-level representations, such as raw values of video pixels and optical flow vector fields and demonstrate the importance of high-quality optical flow estimation for learning accurate action models. We report state-of-the-art results on two challenging benchmarks for human action recognition UCF101 (92.7%) and HMDB51 (67.2%).

中文翻译：

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动作识别的长期时间卷积

典型的人类动作持续几秒钟，并展现出独特的时空结构。最近的方法试图捕获这种结构并通过卷积神经网络学习动作表示。但是，这种表示通常是在几个视频帧的级别上学习的，这些视频帧无法在其整个时间范围内对动作进行建模。在这项工作中，我们使用具有长期时间卷积（LTC）的神经网络学习视频表示。我们证明，随着时间范围的增加，LTC-CNN模型提高了动作识别的准确性。我们还研究了不同低层表示的影响，例如视频像素的原始值和光流矢量场，并证明了高质量光流估计对于学习精确动作模型的重要性。