  Establishing dense correspondences across semantically similar images remains
a challenging task due to the significant intra-class variations and background
clutters. Traditionally, a supervised learning was used for training the
models, which required tremendous manually-labeled data, while some methods
suggested a self-supervised or weakly-supervised learning to mitigate the
reliance on the labeled data, but with limited performance. In this paper, we
present a simple, but effective solution for semantic correspondence that
learns the networks in a semi-supervised manner by supplementing few
ground-truth correspondences via utilization of a large amount of confident
correspondences as pseudo-labels, called SemiMatch. Specifically, our framework
generates the pseudo-labels using the model's prediction itself between source
and weakly-augmented target, and uses pseudo-labels to learn the model again
between source and strongly-augmented target, which improves the robustness of
the model. We also present a novel confidence measure for pseudo-labels and
data augmentation tailored for semantic correspondence. In experiments,
SemiMatch achieves state-of-the-art performance on various benchmarks,
especially on PF-Willow by a large margin.
