Unsupervised Action Segmentation

Unsupervised action segmentation is a challenging task in the field of computer vision, aiming to divide untrimmed video sequences into different action segments without the need for ground truth labels during training. This method discovers the intrinsic structure of actions directly from the data, making it suitable for scenarios where labeled data is limited or there are large-scale unlabeled video datasets. Its results can be further applied to action localization and video summarization, among other tasks, and thus have significant practical value.