Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data
<|reference_start|>Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data: Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a performance and assurance perspective - this being a safety concern in applications such as autonomous vehicles (AVs). This work presents a segmentation network that can detect errors caused by challenging test domains without any additional annotation in a single forward pass. As annotation costs limit the diversity of labelled datasets, we use easy-to-obtain, uncurated and unlabelled data to learn to perform uncertainty estimation by selectively enforcing consistency over data augmentation. To this end, a novel segmentation benchmark based on the SAX Dataset is used, which includes labelled test data spanning three autonomous-driving domains, ranging in appearance from dense urban to off-road. The proposed method, named Gamma-SSL, consistently outperforms uncertainty estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark - by up to 10.7% in area under the receiver operating characteristic (ROC) curve and 19.2% in area under the precision-recall (PR) curve in the most challenging of the three scenarios.<|reference_end|>
@article{williams2024mitigating,
    title={Mitigating Distributional Shift in Semantic Segmentation via Uncertainty
  Estimation from Unlabelled Data},
    author={David S. W. Williams, Daniele De Martini, Matthew Gadd and Paul Newman},
    journal={arXiv preprint arXiv:2402.17653},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17653},
    primaryClass={cs.CV cs.RO}
}