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.

给定相同任务的多个视频，本研究致力于确定任务的关键步骤并确定其顺序。为此，我们提出了一种无监督的基于图的过程学习 (GPL) 框架，其中包括了新颖的 UnityGraph 来获取视频内和视频间上下文。另外，为了获得相似的嵌入，我们使用 Node2Vec 算法无监督地更新 UnityGraph 的嵌入向量。最后，我们使用 KMeans 算法对嵌入向量进行聚类以确定关键步骤。我们在 ProceL、CrossTask 和 EgoProceL 数据集上进行了测试，并相比最先进方法，第三方数据集的平均改进为 2%，EgoProceL 数据集的改进为 3.6%。