This work aims to build pixel-to-pixel correspondences between images from the same
visual class but with different geometries and visual similarities. This task is particularly …

We address the problem of semantic correspondence, that is, establishing a dense flow field
between images depicting different instances of the same object or scene category. We …

Deep features have been proven powerful in building accurate dense semantic
correspondences in various previous works. However, the multi-scale and pyramidal …

Estimating dense visual correspondences between objects with intra-class variation,
deformations and background clutter remains a challenging problem. Thanks to the …

Establishing dense semantic correspondences between object instances remains a
challenging problem due to background clutter, significant scale and pose differences, and …

We address estimating dense correspondences between two images depicting different but
semantically related scenes. End-to-end trainable deep neural networks incorporating …

Dense correspondence across semantically related images has been extensively studied,
but still faces two challenges: 1) large variations in appearance, scale and pose exist even …

We address the problem of semantic correspondence, that is, establishing a dense flow field
between images depicting different instances of the same object or scene category. We …

Establishing precise semantic correspondence across object instances in different images is
a fundamental and challenging task in computer vision. In this task difficulty arises often due …

Establishing dense semantic correspondences requires dealing with large geometric
variations caused by the unconstrained setting of images. To address such severe matching …