Unsupervised Action Segmentation with Self-supervised Feature Learning and Co-occurrence Parsing

• URL: http://arxiv.org/abs/2105.14158v2
• Date: Wed, 2 Jun 2021 15:14:27 GMT
• Title: Unsupervised Action Segmentation with Self-supervised Feature Learning and Co-occurrence Parsing
• Authors: Zhe Wang, Hao Chen, Xinyu Li, Chunhui Liu, Yuanjun Xiong, Joseph Tighe, Charless Fowlkes
• Abstract summary: temporal action segmentation is a task to classify each frame in the video with an action label. In this work we explore a self-supervised method that operates on a corpus of unlabeled videos and predicts a likely set of temporal segments across the videos. We develop CAP, a novel co-occurrence action parsing algorithm that can not only capture the correlation among sub-actions underlying the structure of activities, but also estimate the temporal trajectory of the sub-actions in an accurate and general way.
• Score: 32.66011849112014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 explore a self-supervised method that operates on a corpus of unlabeled videos and predicts a likely set of temporal segments across the videos. To do this we leverage self-supervised video classification approaches to perform unsupervised feature extraction. On top of these features we develop CAP, a novel co-occurrence action parsing algorithm that can not only capture the correlation among sub-actions underlying the structure of activities, but also estimate the temporal trajectory of the sub-actions in an accurate and general way. We evaluate on both classic datasets (Breakfast, 50Salads) and emerging fine-grained action datasets (FineGym) with more complex activity structures and similar sub-actions. Results show that our method achieves state-of-the-art performance on all three datasets with up to 22\% improvement, and can even outperform some weakly-supervised approaches, demonstrating its effectiveness and generalizability.

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