We introduce a novel approach for temporal activity segmentation with
timestamp supervision. Our main contribution is a graph convolutional network,
which is learned in an end-to-end manner to exploit both frame features and
connections between neighboring frames to generate dense framewise labels from
sparse timestamp labels. The generated dense framewise labels can then be used
to train the segmentation model. In addition, we propose a framework for
alternating learning of both the segmentation model and the graph convolutional
model, which first initializes and then iteratively refines the learned models.
Detailed experiments on four public datasets, including 50 Salads, GTEA,
Breakfast, and Desktop Assembly, show that our method is superior to the
multi-layer perceptron baseline, while performing on par with or better than
the state of the art in temporal activity segmentation with timestamp
supervision.

本文介绍了一种使用时间戳监督进行时间活动分割的新方法，其中引入一个图卷积网络，以端到端的方式学习利用帧特征和相邻帧之间的连接生成密集帧标签的方法。我们还提出了一个交替学习的框架，用于初始化和迭代地完善学习模型。在 50Salads、GTEA、Breakfast、Desktop Assembly 等公共数据集上的详细实验表明，我们的方法优于多层感知器基线，同时在时间活动分割方面表现不输于或优于现有技术。