Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset. Thus in this work we propose an unsupervised method, namely SSCAP, that operates on a corpus of unlabeled videos and predicts a likely set of temporal segments across the videos. SSCAP leverages Self-Supervised learning to extract distinguishable features and then applies a novel Co-occurrence Action Parsing algorithm to not only capture the correlation among sub-actions underlying the structure of activities, but also estimate the temporal path of the sub-actions in an accurate and general way. We evaluate on both classic datasets (Breakfast, 50Salads) and the emerging fine-grained action dataset (FineGym) with more complex activity structures and similar sub-actions. Results show that SSCAP achieves state-of-the-art performance on all datasets and can even outperform some weakly-supervised approaches, demonstrating its effectiveness and generalizability.
@article{arxiv.2105.14158,
title = {SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation},
author = {Zhe Wang and Hao Chen and Xinyu Li and Chunhui Liu and Yuanjun Xiong and Joseph Tighe and Charless Fowlkes},
journal= {arXiv preprint arXiv:2105.14158},
year = {2021}
}