Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation

• URL: http://arxiv.org/abs/2404.01518v3
• Date: Mon, 8 Apr 2024 05:09:19 GMT
• Title: Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation
• Authors: Ming Xu, Stephen Gould,
• Abstract summary: We propose a novel approach to the action segmentation task for long, untrimmed videos. By encoding a temporal consistency prior to a Gromov-Wasserstein problem, we are able to decode a temporally consistent segmentation. Our method does not require knowing the action order for a video to attain temporal consistency.
• Score: 31.622109513774635
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a novel approach to the action segmentation task for long, untrimmed videos, based on solving an optimal transport problem. By encoding a temporal consistency prior into a Gromov-Wasserstein problem, we are able to decode a temporally consistent segmentation from a noisy affinity/matching cost matrix between video frames and action classes. Unlike previous approaches, our method does not require knowing the action order for a video to attain temporal consistency. Furthermore, our resulting (fused) Gromov-Wasserstein problem can be efficiently solved on GPUs using a few iterations of projected mirror descent. We demonstrate the effectiveness of our method in an unsupervised learning setting, where our method is used to generate pseudo-labels for self-training. We evaluate our segmentation approach and unsupervised learning pipeline on the Breakfast, 50-Salads, YouTube Instructions and Desktop Assembly datasets, yielding state-of-the-art results for the unsupervised video action segmentation task.

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