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Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods

conference contribution
posted on 2023-11-29, 18:09 authored by Apratim Bhattacharyya, Mario FritzMario Fritz, Bernt Schiele
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.


Preferred Citation

Apratim Bhattacharyya, Mario Fritz and Bernt Schiele. Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods. In: International Conference on Learning Representations (ICLR). 2019.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

International Conference on Learning Representations (ICLR)

Legacy Posted Date


Open Access Type

  • Gold


@inproceedings{cispa_all_2793, title = "Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods", author = "Bhattacharyya, Apratim and Fritz, Mario and Schiele, Bernt", booktitle="{International Conference on Learning Representations (ICLR)}", year="2019", }

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