Visual Forecasting by Imitating Dynamics in Natural Sequences

ICCV 2017 Kuo-Hao ZengWilliam B. ShenDe-An HuangMin SunJuan Carlos Niebles

We introduce a general framework for visual forecasting, which directly imitates visual sequences without additional supervision. As a result, our model can be applied at several semantic levels and does not require any domain knowledge or handcrafted features... (read more)

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