  In this paper we address the problem of automatically discovering atomic
actions in unsupervised manner from instructional videos. Instructional videos
contain complex activities and are a rich source of information for intelligent
agents, such as, autonomous robots or virtual assistants, which can, for
example, automatically `read' the steps from an instructional video and execute
them. However, videos are rarely annotated with atomic activities, their
boundaries or duration. We present an unsupervised approach to learn atomic
actions of structured human tasks from a variety of instructional videos. We
propose a sequential stochastic autoregressive model for temporal segmentation
of videos, which learns to represent and discover the sequential relationship
between different atomic actions of the task, and which provides automatic and
unsupervised self-labeling for videos. Our approach outperforms the
state-of-the-art unsupervised methods with large margins. We will open source
the code.
