Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling
<|reference_start|>Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling: This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach, which is robust to augmentation hyper-parameters and simpler than previous techniques. For neural networks used in safety-critical applications, bias in the training data can lead to errors; therefore it is crucial to understand a network's limitations at run time and act accordingly. To this end, we test our proposed method on a number of test domains including the SAX Segmentation benchmark, which includes labelled test data from dense urban, rural and off-road driving domains. The proposed method consistently outperforms uncertainty estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark.<|reference_end|>
@article{williams2024masked,
    title={Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image
  Modeling},
    author={David S. W. Williams, Matthew Gadd, Paul Newman and Daniele De Martini},
    journal={arXiv preprint arXiv:2402.17622},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17622},
    primaryClass={cs.CV cs.RO}
}