Our weakly-supervised results show that: (1) Both action segmentation metrics -all label accuracy and step label accuracy -are important to evaluate whether models adequately distinguish meaningful actions from background.
(3) ordering supervision alone is not sufficient to allow these models to learn better segmentations than a simple baseline that just uses the ordering to assign labels (ordered uniform); narration is also required.