We address the problem of extracting key steps from unlabeled procedural
videos, motivated by the potential of Augmented Reality (AR) headsets to
revolutionize job training and performance. We decompose the problem into two
steps: representation learning and key steps extraction. We propose a training
objective, Bootstrapped Multi-Cue Contrastive (BMC2) loss to learn
disciriminative representations for various steps without any labels. Different
from prior works, we develop techniques to train a light-weight temporal module
which uses off-the-shelf features for self supervision. Our approach can
seamlessly leverage information from multiple cues like optical flow, depth or
gaze to learn discriminative features for key-steps making it amenable for AR
applications. We finally extract key steps via a tunable algorithm that
clusters the representations and samples. We show significant improvements over
prior works for the task of key step localization and phase classification.
Qualitative results demonstrate that the extracted key steps are meaningful to
succinctly represent various steps of the procedural tasks.

通过提出的 Bootstrapped Multi-Cue Contrastive (BMC2) Loss，我们成功地建立了一个轻量级的时间模型，该模型使用现成的特征进行自我监督，并能够从多个提示信号中获取信息，最终实现从无标记程序性视频中提取出有意义的关键步骤。