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The abundance of instructional videos and their narrations over the Internetoffers an exciting avenue for understanding procedural activities. In thiswork, we propose to learn video representation that encodes both action stepsand their temporal ordering, based on a large-scale dataset of webinstructional videos and their narrations, without using human annotations. Ourmethod jointly learns a video representation to encode individual stepconcepts, and a deep probabilistic model to capture both temporal dependenciesand immense individual variations in the step ordering. We empiricallydemonstrate that learning temporal ordering not only enables new capabilitiesfor procedure reasoning, but also reinforces the recognition of individualsteps. Our model significantly advances the state-of-the-art results on stepclassification (+2.8% / +3.3% on COIN / EPIC-Kitchens) and step forecasting(+7.4% on COIN). Moreover, our model attains promising results in zero-shotinference for step classification and forecasting, as well as in predictingdiverse and plausible steps for incomplete procedures. Our code is available athttps://github.com/facebookresearch/ProcedureVRL.

Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations