  Dense correspondence across semantically related images has been extensively
studied, but still faces two challenges: 1) large variations in appearance,
scale and pose exist even for objects from the same category, and 2) labeling
pixel-level dense correspondences is labor intensive and infeasible to scale.
Most existing approaches focus on designing various matching approaches with
fully-supervised ImageNet pretrained networks. On the other hand, while a
variety of self-supervised approaches are proposed to explicitly measure
image-level similarities, correspondence matching the pixel level remains
under-explored. In this work, we propose a multi-level contrastive learning
approach for semantic matching, which does not rely on any ImageNet pretrained
model. We show that image-level contrastive learning is a key component to
encourage the convolutional features to find correspondence between similar
objects, while the performance can be further enhanced by regularizing
cross-instance cycle-consistency at intermediate feature levels. Experimental
results on the PF-PASCAL, PF-WILLOW, and SPair-71k benchmark datasets
demonstrate that our method performs favorably against the state-of-the-art
approaches. The source code and trained models will be made available to the
public.
