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dc.contributor.authorVondrick, Carl
dc.contributor.authorPirsiavash, Hamed
dc.contributor.authorTorralba, Antonio
dc.date.accessioned2019-05-24T15:48:17Z
dc.date.available2019-05-24T15:48:17Z
dc.date.issued2016
dc.description30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spainen_US
dc.description.abstractWe capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene’s foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.en_US
dc.description.sponsorshipThis work was supported by NSF grant #1524817 to AT, START program at UMBC to HP, and the Google PhD fellowship to CV.en_US
dc.description.urihttps://papers.nips.cc/paper/6194-generating-videos-with-scene-dynamics.pdfen_US
dc.format.extent9 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m26gih-tnyz
dc.identifier.citationCarl Vondrick, Hamed Pirsiavash, Antonio Torralba, Generating Videos with Scene Dynamics, Advances in Neural Information Processing Systems 29 (NIPS 2016),https://papers.nips.cc/paper/6194-generating-videos-with-scene-dynamics.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/13942
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectvideo recognition tasksen_US
dc.subjectspatio-temporal convolutional architectureen_US
dc.subjectvisualizationsen_US
dc.titleGenerating Videos with Scene Dynamicsen_US
dc.typeTexten_US


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