  Temporal action segmentation is typically achieved by discovering the
dramatic variances in global visual descriptors. In this paper, we explore the
merits of local features by proposing the unsupervised framework of
Object-centric Temporal Action Segmentation (OTAS). Broadly speaking, OTAS
consists of self-supervised global and local feature extraction modules as well
as a boundary selection module that fuses the features and detects salient
boundaries for action segmentation. As a second contribution, we discuss the
pros and cons of existing frame-level and boundary-level evaluation metrics.
Through extensive experiments, we find OTAS is superior to the previous
state-of-the-art method by $41\%$ on average in terms of our recommended F1
score. Surprisingly, OTAS even outperforms the ground-truth human annotations
in the user study. Moreover, OTAS is efficient enough to allow real-time
inference.
