Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation

• URL: http://arxiv.org/abs/2103.11264v2
• Date: Tue, 23 Mar 2021 08:16:53 GMT
• Title: Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
• Authors: M. Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, Rainer Stiefelhagen
• Abstract summary: Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video.
• Score: 96.67525775629444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this and other video understanding tasks, supervised approaches have achieved encouraging performance but require a high volume of detailed frame-level annotations. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video. Our main finding is that representing a video with a 1-nearest neighbor graph by taking into account the time progression is sufficient to form semantically and temporally consistent clusters of frames where each cluster may represent some action in the video. Additionally, we establish strong unsupervised baselines for action segmentation and show significant performance improvements over published unsupervised methods on five challenging action segmentation datasets. Our approach also outperforms weakly-supervised methods by large margins on 4 of these datasets. Interestingly, we also achieve better results than many fully-supervised methods that have reported results on these datasets. Our code is available at https://github.com/ssarfraz/FINCH-Clustering/tree/master/TW-FINCH

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