  This paper addresses the problem of establishing semantic correspondences
between images depicting different instances of the same object or scene
category. Previous approaches focus on either combining a spatial regularizer
with hand-crafted features, or learning a correspondence model for appearance
only. We propose instead a convolutional neural network architecture, called
SCNet, for learning a geometrically plausible model for semantic
correspondence. SCNet uses region proposals as matching primitives, and
explicitly incorporates geometric consistency in its loss function. It is
trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and
a comparative evaluation on several standard benchmarks demonstrates that the
proposed approach substantially outperforms both recent deep learning
architectures and previous methods based on hand-crafted features.
