AutoCorrect: Deep inductive alignment of noisy geometric annotations
We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise. The method is based on a consistency loss that enables deep neural networks to be trained, given only noisy annotations as input, to correct the annotatio...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
British Machine Vision Association
2020
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