Unsupervised Action Segmentation

Unsupervised Action Segmentation is a challenging problem in high-level video understanding, where the goal is to segment a temporally untrimmed sequence into distinct action segments without access to ground truth labels during training. Unlike supervised methods, which rely on annotated datasets, unsupervised approaches aim to discover the underlying structure of actions directly from data. This makes the task particularly valuable for scenarios with limited labeled data or large-scale unlabeled video datasets. The results of Unsupervised Action Segmentation can be further applied to tasks such as action localization and video summarization.