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 annotations.
When some noise-free annotations are available, we show that the consistency
loss reduces to a stricter self-supervised loss. We also show that the method
can implicitly leverage object symmetries to reduce the ambiguity arising in
correcting noisy annotations. When multiple object-annotation pairs are present
in an image, we introduce a spatial memory map that allows the network to
correct annotations sequentially, one at a time, while accounting for all other
annotations in the image and corrections performed so far. Through ablation, we
show the benefit of these contributions, demonstrating excellent results on
geo-spatial imagery. Specifically, we show results using a new Railway tracks
dataset as well as the public INRIA Buildings benchmarks, achieving new
state-of-the-art results for the latter.Comment: BMVC 2019 (Spotlight
