  Establishing visual correspondences under large intra-class variations, which
is often referred to as semantic correspondence or semantic matching, remains a
challenging problem in computer vision. Despite its significance, however, most
of the datasets for semantic correspondence are limited to a small amount of
image pairs with similar viewpoints and scales. In this paper, we present a new
large-scale benchmark dataset of semantically paired images, SPair-71k, which
contains 70,958 image pairs with diverse variations in viewpoint and scale.
Compared to previous datasets, it is significantly larger in number and
contains more accurate and richer annotations. We believe this dataset will
provide a reliable testbed to study the problem of semantic correspondence and
will help to advance research in this area. We provide the results of recent
methods on our new dataset as baselines for further research. Our benchmark is
available online at http://cvlab.postech.ac.kr/research/SPair-71k/.
