Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Weakly Supervised Action Segmentation Using Mutual Consistency   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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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex