Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

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 …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Multi-modal image registration aims to spatially align two images from different modalities to
make their feature points match with each other. Captured by different sensors, the images …

Recent research on learned visual descriptors has shown promising improvements in
correspondence estimation, a key component of many 3D vision tasks. However, existing …

Establishing dense correspondences between a pair of images is an important and general
problem, covering geometric matching, optical flow and semantic correspondences. While …

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel
class in the query image with only a few annotated support images. The current correlation …

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 …

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

We introduce DiscoBox, a novel framework that jointly learns instance segmentation and
semantic correspondence using bounding box supervision. Specifically, we propose a self …