Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling
This addresses the need for reliable uncertainty estimation in neural networks for safety-critical domains like autonomous driving, though it appears incremental as it builds on existing Masked Image Modeling approaches.
The paper tackles the problem of producing high-quality uncertainty estimates for semantic segmentation in safety-critical applications, and the result is a method that consistently outperforms existing uncertainty estimation and Out-of-Distribution techniques on benchmarks like SAX Segmentation.
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.