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 …

Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …

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 …

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 …

Few-shot semantic segmentation aims at learning to segment a target object from a query
image using only a few annotated support images of the target class. This challenging task …

The goal of self-supervised learning from images is to construct image representations that
are semantically meaningful via pretext tasks that do not require semantic annotations. Many …

In this work we address the problem of finding reliable pixel-level correspondences under
difficult imaging conditions. We propose an approach where a single convolutional neural …

Thin, reflective objects such as forks and whisks are common in our daily lives, but they are
particularly chal-lenging for robot perception because it is hard to reconstruct them using …