We propose a novel cost aggregation network, called Cost Aggregation Transformers
(CATs), to find dense correspondences between semantically similar images with additional …

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

In this paper, we tackle the task of establishing dense visual correspondences between
images containing objects of the same category. This is a challenging task due to large intra …

The key challenge in learning dense correspondences lies in the lack of ground-truth
matches for real image pairs. While photometric consistency losses provide unsupervised …

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 …

Generating robust and reliable correspondences across images is a fundamental task for a
diversity of applications. To capture context at both global and local granularity, we propose …

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

Learning visual similarity requires to learn relations, typically between triplets of images.
Albeit triplet approaches being powerful, their computational complexity mostly limits training …

This paper presents a deep architecture for dense semantic correspondence, called
pyramidal affine regression networks (PARN), that estimates locally-varying affine …

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