GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network

Open access
Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
The feature correlation layer serves as a key neural network module in numerous computer vision problems that involve dense correspondences between image pairs. It predicts a correspondence volume by evaluating dense scalar products between feature vectors extracted from pairs of locations in two images.
However, this point-to-point feature comparison is insufficient when disambiguating multiple similar regions in an image, severely affecting the performance of the end task.
We propose GOCor, a fully differentiable dense matching module, acting as a direct replacement to the feature correlation layer.
The correspondence volume generated by our module is the result of an internal optimization procedure that explicitly accounts for similar regions in the scene. Moreover, our approach is capable of effectively learning spatial matching priors to resolve further matching ambiguities.
We analyze our GOCor module in extensive ablative experiments. When integrated into state-of-the-art networks, our approach significantly outperforms the feature correlation layer for the tasks of geometric matching, optical flow, and dense semantic matching. The code and trained models will be made available at github.com/PruneTruong/GOCor Show more
Permanent link
https://doi.org/10.3929/ethz-b-000455564Publication status
publishedExternal links
Book title
Advances in Neural Information Processing Systems 33Pages / Article No.
Publisher
CurranEvent
Subject
computer vision; machine learningOrganisational unit
03514 - Van Gool, Luc / Van Gool, Luc
Notes
Poster presentation held on December 8, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.More
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ETH Bibliography
yes
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