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Learning to Segment Actions from Observation and Narration

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arxiv 2005.03684 v2 pith:3BBGUQXH submitted 2020-05-07 cs.CL cs.CV

Learning to Segment Actions from Observation and Narration

Daniel Fried , Jean-Baptiste Alayrac , Phil Blunsom , Chris Dyer , Stephen Clark , Aida Nematzadeh This is my paper
classification cs.CL cs.CV
keywords modelactionnarrationsegmentationstructuretasktrainingactions
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We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.

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  1. REMAP: Regularized Matching and Partial Alignment of Video Embeddings

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    REMAP applies regularized fused partial Gromov-Wasserstein optimal transport to align video embeddings for unsupervised procedure learning on noisy instructional videos.