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• W2890421420 abstract "Most adversarial learning based video prediction methods suffer from image blur, since the commonly used adversarial and regression loss pair work rather in a competitive way than collaboration, yielding compromised blur effect. In the meantime, as often relying on a single-pass architecture, the predictor is inadequate to explicitly capture the forthcoming uncertainty. Our work involves two key insights: (1) Video prediction can be approached as a stochastic process: we sample a collection of proposals conforming to possible frame distribution at following time stamp, and one can select the final prediction from it. (2) De-coupling combined loss functions into dedicatedly designed sub-networks encourages them to work in a collaborative way. Combining above two insights we propose a two-stage network called VPSS (textbf{V}ideo textbf{P}rediction via textbf{S}elective textbf{S}ampling). Specifically a emph{Sampling} module produces a collection of high quality proposals, facilitated by a multiple choice adversarial learning scheme, yielding diverse frame proposal set. Subsequently a emph{Selection} module selects high possibility candidates from proposals and combines them to produce final prediction. Extensive experiments on diverse challenging datasets demonstrate the effectiveness of proposed video prediction approach, i.e., yielding more diverse proposals and accurate prediction results." @default.
• W2890421420 created "2018-09-27" @default.
• W2890421420 creator A5014362734 @default.
• W2890421420 creator A5019708391 @default.
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• W2890421420 date "2018-01-01" @default.
• W2890421420 modified "2023-09-28" @default.
• W2890421420 title "Video Prediction via Selective Sampling" @default.
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