Despite advances in feature representation, leveraging geometric relations is crucial for
establishing reliable visual correspondences under large variations of images. In this work …

Finding dense semantic correspondence is a fundamental problem in computer vision,
which remains challenging in complex scenes due to background clutter, extreme intra-class …

Finding visual correspondence between local features is key to many computer vision
problems. While defining features with larger contextual scales usually implies greater …

Establishing dense correspondences across semantically similar images is a challenging
task. Due to the large intra-class variation and background clutter, two common issues occur …

Feature matching, which refers to identifying and then corresponding the same or similar
visual pattern from two or more images, is a key technique in any image processing task that …

There is a long-standing problem of repeated patterns in correspondence problems, where
mismatches frequently occur because of inherent ambiguity. The unique position information …

Establishing superior-quality correspondences in an image pair is pivotal to many
subsequent computer vision tasks. Using Euclidean distance between correspondences to …

Establishing visual correspondences under large intra-class variations requires analyzing
images at different levels, from features linked to semantics and context to local patterns …

The feature correlation layer serves as a key neural network module in numerous computer
vision problems that involve dense correspondences between image pairs. It predicts a …

Identifying robust and accurate correspondences across images is a fundamental problem
in computer vision that enables various downstream tasks. Recent semi-dense matching …