Temporal action segmentation is a topic of increasing interest, however,
annotating each frame in a video is cumbersome and costly. Weakly supervised
approaches therefore aim at learning temporal action segmentation from videos
that are only weakly labeled. In this work, we assume that for each training
video only the list of actions is given that occur in the video, but not when,
how often, and in which order they occur. In order to address this task, we
propose an approach that can be trained end-to-end on such data. The approach
divides the video into smaller temporal regions and predicts for each region
the action label and its length. In addition, the network estimates the action
labels for each frame. By measuring how consistent the frame-wise predictions
are with respect to the temporal regions and the annotated action labels, the
network learns to divide a video into class-consistent regions. We evaluate our
approach on three datasets where the approach achieves state-of-the-art
results.

本文提出了一种弱监督学习的端到端方法，将视频分成小的时间段，并预测每个时间段的动作标签及其长度。通过测量时间段与注释动作标签之间的一致性，该方法可学习将视频划分为类一致的区域，并在三个数据集上实现了最先进的结果。