Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addressing real-time application is amplified by the need to operate on resource constrained hardware. Whilst development of real-time methods for these platforms has increased, these models are unable to sufficiently reason about uncertainty present when applied on embedded real-time systems. This paper addresses this by combining deep feature extraction from pre-trained models with Bayesian regression and moment propagation for uncertainty aware predictions. We demonstrate how the proposed method can yield meaningful epistemic uncertainty on embedded hardware in real-time whilst maintaining predictive performance.

翻译：在自动驾驶和人机交互等领域的语义分割模型应用中，需要具备实时预测能力。实时应用的挑战因需在资源受限硬件上运行而进一步加剧。尽管针对这些平台的实时方法开发有所增加，但这些模型在应用于嵌入式实时系统时，无法充分推理存在的不确定性。本文通过将预训练模型的深度特征提取与贝叶斯回归及矩传播相结合，实现了不确定性感知预测。我们展示了所提方法如何在保持预测性能的同时，在嵌入式硬件上实时生成有意义的认知不确定性。