Abstract We propose Probabilistic Warp Consistency, a weakly-supervised learning
objective for semantic matching. Our approach directly supervises the dense matching …

Establishing dense correspondences across semantically similar images remains a
challenging task due to the significant intra-class variations and background clutters …

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

Despite significant progress of deep learning in recent years, state-of-the-art semantic
matching methods still rely on legacy features such as SIFT or HoG. We argue that the …

In this paper we address the challenge of matching semantically similar keypoints across
image pairs. Existing research indicates that the intermediate output of the UNet within the …

Establishing visual correspondences under large intra-class variations, which is often
referred to as semantic correspondence or semantic matching, remains a challenging …

We explore semantic correspondence estimation through the lens of unsupervised learning.
We thoroughly evaluate several recently proposed unsupervised methods across multiple …

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

Establishing correspondences between images remains a challenging task, especially
under large appearance changes due to different viewpoints or intra-class variations. In this …