  Given multiple videos of the same task, procedure learning addresses
identifying the key-steps and determining their order to perform the task. For
this purpose, existing approaches use the signal generated from a pair of
videos. This makes key-steps discovery challenging as the algorithms lack
inter-videos perspective. Instead, we propose an unsupervised Graph-based
Procedure Learning (GPL) framework. GPL consists of the novel UnityGraph that
represents all the videos of a task as a graph to obtain both intra-video and
inter-videos context. Further, to obtain similar embeddings for the same
key-steps, the embeddings of UnityGraph are updated in an unsupervised manner
using the Node2Vec algorithm. Finally, to identify the key-steps, we cluster
the embeddings using KMeans. We test GPL on benchmark ProceL, CrossTask, and
EgoProceL datasets and achieve an average improvement of 2% on third-person
datasets and 3.6% on EgoProceL over the state-of-the-art.
