Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation
<|reference_start|>Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation: 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.<|reference_end|>
@article{xu2024temporally,
    title={Temporally Consistent Unbalanced Optimal Transport for Unsupervised
  Action Segmentation},
    author={Ming Xu, Stephen Gould},
    journal={arXiv preprint arXiv:2404.01518},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01518},
    primaryClass={cs.CV cs.LG eess.IV}
}