Unsupervised Discovery of Actions in Instructional Videos

In this paper we address the problem of automatically discovering atomic actions 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 actions of the task, and provides automatic and unsupervised self-labeling. We evaluate on the breakfast, 50-salads and narrated instructional videos datasets. Code will be open sourced.