  We present semantic attribute matching networks (SAM-Net) for jointly
establishing correspondences and transferring attributes across semantically
similar images, which intelligently weaves the advantages of the two tasks
while overcoming their limitations. SAM-Net accomplishes this through an
iterative process of establishing reliable correspondences by reducing the
attribute discrepancy between the images and synthesizing attribute transferred
images using the learned correspondences. To learn the networks using weak
supervisions in the form of image pairs, we present a semantic attribute
matching loss based on the matching similarity between an attribute transferred
source feature and a warped target feature. With SAM-Net, the state-of-the-art
performance is attained on several benchmarks for semantic matching and
attribute transfer.
