  Semantic correspondence is the problem of establishing correspondences across
images depicting different instances of the same object or scene class. One of
recent approaches to this problem is to estimate parameters of a global
transformation model that densely aligns one image to the other. Since an
entire correlation map between all feature pairs across images is typically
used to predict such a global transformation, noisy features from different
backgrounds, clutter, and occlusion distract the predictor from correct
estimation of the alignment. This is a challenging issue, in particular, in the
problem of semantic correspondence where a large degree of image variations is
often involved. In this paper, we introduce an attentive semantic alignment
method that focuses on reliable correlations, filtering out distractors. For
effective attention, we also propose an offset-aware correlation kernel that
learns to capture translation-invariant local transformations in computing
correlation values over spatial locations. Experiments demonstrate the
effectiveness of the attentive model and offset-aware kernel, and the proposed
model combining both techniques achieves the state-of-the-art performance.
