Masked γ-SSL: learning uncertainty estimation via masked image modeling

Masked γ-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-parame...

書誌詳細
主要な著者:	Williams, DSW, Gadd, M, Newman, P, De Martini, D
フォーマット:	Conference item
言語:	English
出版事項:	IEEE 2024

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