Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

Text-to-image diffusion models have made significant advances in generating and editing
high-quality images. As a result, numerous approaches have explored the ability of diffusion …

This paper presents a novel cost aggregation network, called Volumetric Aggregation with
Transformers (VAT), for few-shot segmentation. The use of transformers can benefit …

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 …

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

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

We study the problem of learning semantic image correspondences without manual
supervision. Previous works that tackled this problem rely on manually curated image pairs …

The commonly adopted detect-then-match approach to registration finds difficulties in the
cross-modality cases due to the incompatible keypoint detection and inconsistent feature …

While pre-trained large-scale vision models have shown significant promise for semantic
correspondence their features often struggle to grasp the geometry and orientation of …

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