  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.
