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.