Uncertainty in Real-Time Semantic Segmentation on Embedded Systems

CVPRW 2023 pp. 4491-4501

doi:10.1109/CVPRW59228.2023.00472 /cvprw/2023/goan2023cvprw-uncertainty/

Abstract

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.

PDF CVPRW Semantic Scholar

Cite

Text

Goan and Fookes. "Uncertainty in Real-Time Semantic Segmentation on Embedded Systems." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00472

Markdown

[Goan and Fookes. "Uncertainty in Real-Time Semantic Segmentation on Embedded Systems." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/goan2023cvprw-uncertainty/) doi:10.1109/CVPRW59228.2023.00472

BibTeX

@inproceedings{goan2023cvprw-uncertainty,
  title     = {{Uncertainty in Real-Time Semantic Segmentation on Embedded Systems}},
  author    = {Goan, Ethan and Fookes, Clinton},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {4491-4501},
  doi       = {10.1109/CVPRW59228.2023.00472},
  url       = {https://mlanthology.org/cvprw/2023/goan2023cvprw-uncertainty/}
}