  For autonomous agents to successfully operate in the real world, the ability
to anticipate future scene states is a key competence. In real-world scenarios,
future states become increasingly uncertain and multi-modal, particularly on
long time horizons. Dropout based Bayesian inference provides a computationally
tractable, theoretically well grounded approach to learn likely
hypotheses/models to deal with uncertain futures and make predictions that
correspond well to observations -- are well calibrated. However, it turns out
that such approaches fall short to capture complex real-world scenes, even
falling behind in accuracy when compared to the plain deterministic approaches.
This is because the used log-likelihood estimate discourages diversity. In this
work, we propose a novel Bayesian formulation for anticipating future scene
states which leverages synthetic likelihoods that encourage the learning of
diverse models to accurately capture the multi-modal nature of future scene
states. We show that our approach achieves accurate state-of-the-art
predictions and calibrated probabilities through extensive experiments for
scene anticipation on Cityscapes dataset. Moreover, we show that our approach
generalizes across diverse tasks such as digit generation and precipitation
forecasting.
