# AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations

arXiv: https://arxiv.org/abs/1908.05263 · HTML: https://mlsanity.com/paper/1908.05263/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Single View Geocentric Pose in the Wild](https://mlsanity.com/paper/2105.08229/) (2021)
- [LaneCorrect: Self-supervised Lane Detection](https://mlsanity.com/paper/2404.14671/) (2024)
- [Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels](https://mlsanity.com/paper/2607.10841/) (2026)

## Abstract (arXiv)

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.