We address the problem of automatically learning the main steps to complete a
certain task, such as changing a car tire, from a set of narrated instruction
videos. The contributions of this paper are three-fold. First, we develop a new
unsupervised learning approach that takes advantage of the complementary nature
of the input video and the associated narration. The method solves two
clustering problems, one in text and one in video, applied one after each other
and linked by joint constraints to obtain a single coherent sequence of steps
in both modalities. Second, we collect and annotate a new challenging dataset
of real-world instruction videos from the Internet. The dataset contains about
800,000 frames for five different tasks that include complex interactions
between people and objects, and are captured in a variety of indoor and outdoor
settings. Third, we experimentally demonstrate that the proposed method can
automatically discover, in an unsupervised manner, the main steps to achieve
the task and locate the steps in the input videos.

本文提出一种基于非监督学习的方法，通过集合指导语音和视频，可以自动地学习完成某项任务的主要步骤，例如更换汽车轮胎。通过应用两个聚类问题，同时解决文本和视频中的信息，使它们连接在一起，得出在两种情况下都具有一致性的主要步骤。本文提出的方法可以自动地在输入视频中发现和定位处于任务中的主要步骤。作者还采集和注解了一个包含旨在模拟真实场景的任务的挑战数据集。