Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning
In this work, a neural network is trained to simultaneously perform segmentation and pixel-wise Out-of-Distribution (OoD) detection, such that the segmentation of unknown regions of scenes can be rejected. This is made possible by leveraging an OoD dataset with a novel contrastive objective and data...
| Главные авторы: | , , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
IEEE
2021
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