The dominant paradigm for video-based action segmentation is composed of two
steps: first, for each frame, compute low-level features using Dense
Trajectories or a Convolutional Neural Network that encode spatiotemporal
information locally, and second, input these features into a classifier that
captures high-level temporal relationships, such as a Recurrent Neural Network
(RNN). While often effective, this decoupling requires specifying two separate
models, each with their own complexities, and prevents capturing more nuanced
long-range spatiotemporal relationships. We propose a unified approach, as
demonstrated by our Temporal Convolutional Network (TCN), that hierarchically
captures relationships at low-, intermediate-, and high-level time-scales. Our
model achieves superior or competitive performance using video or sensor data
on three public action segmentation datasets and can be trained in a fraction
of the time it takes to train an RNN.

提出了一种统一的时间卷积网络方法，可以逐层捕捉低、中、高层次时间尺度上的关系，在三个公共操作分割数据集上以视频或传感器数据实现了优越或竞争性能，并且可以在较短时间内训练完成。