Probabilistic Video Prediction From Noisy Data With a Posterior Confidence

CVPR 2020 Yunbo Wang Jiajun Wu Mingsheng Long Joshua B. Tenenbaum

We study a new research problem of probabilistic future frames prediction from a sequence of noisy inputs, which is useful because it is difficult to guarantee the quality of input frames in practical spatiotemporal prediction applications. It is also challenging because it involves two levels of uncertainty: the perceptual uncertainty from noisy observations and the dynamics uncertainty in forward modeling... (read more)

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