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arXiv · 1904.03116

Fast Weakly Supervised Action Segmentation Using Mutual Consistency

Yaser Souri·Mohsen Fayyaz·Luca Minciullo·Gianpiero Francesca·Juergen Gall
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Abstract

Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In this paper, we propose a novel end-to-end approach for weakly supervised action segmentation based on a two-branch neural network. The two branches of our network predict two redundant but different representations for action segmentation and we propose a novel mutual consistency (MuCon) loss that enforces the consistency of the two redundant representations. Using the MuCon loss together with a loss for transcript prediction, our proposed approach achieves the accuracy of state-of-the-art approaches while being $14$ times faster to train and $20$ times faster during inference. The MuCon loss proves beneficial even in the fully supervised setting.

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Yaser Souri, Mohsen Fayyaz, Luca Minciullo, Gianpiero Francesca, Juergen Gall. 2019-04-05. Fast Weakly Supervised Action Segmentation Using Mutual Consistency. https://arxiv.org/abs/1904.03116

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