  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.
