What does this repo do?

Implements methods for training semantic segmentation networks to perform high-quality uncertainty estimation on distributionally-shifted data. It does this by training on unlabelled distributionally-shifted images with a self-supervised task, and learning to detect segmentation inconsistency as a proxy for segmentation error.

./scripts/train_deeplab_gssl.sh represents our first method to do this in GammaSSL, and then ./scripts/train_vit_mgssl.sh represents our second method MaskedGammaSSL.

Testing the quality of a model's uncertainty estimation can be performed using ue_testing.

The required conda environment can be setup with:

conda env create -f environment.yml
conda activate gammassl

Papers

“Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data”, D. Williams, D. De Martini, M. Gadd, and P. Newman, IEEE Transactions on Robotics (T-RO), 2024

@article{gammassl,
title={{Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data}},
author={Williams, David and De Martini, Daniele and Gadd, Matthew and Newman, Paul},
booktitle={IEEE Transactions on Robotics (T-RO)},
year={2024},
}

“Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling”, D. Williams, M. Gadd, P. Newman, and D. De Martini, IEEE International Conference on Robotics and Automation (ICRA), 2024

@article{maskedgammassl,
title={{Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling}},
author={Williams, David and Gadd, Matthew and Newman, Paul and De Martini, Daniele},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2024},
}