BriefGPT.xyz
Oct, 2018
使用合成似然的贝叶斯方法预测未来街道场景
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
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Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
TL;DR
本文提出了一种基于贝叶斯推断和多模式数据的方法,用于学习场景的未来状态,并展示了其在城市景观场景预测、数字生成和降水预测等领域的应用。
Abstract
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
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