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 …

Transformer trackers have achieved impressive advancements recently, where the attention
mechanism plays an important role. However, the independent correlation computation in …

We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …

We present a new test-time optimization method for estimating dense and long-range motion
from a video sequence. Prior optical flow or particle video tracking algorithms typically …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

We present a novel method for local image feature matching. Instead of performing image
feature detection, description, and matching sequentially, we propose to first establish pixel …

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 …

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

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting
unseen classes with only a handful of annotations. Previous methods limited to the semantic …

We propose to address the problem of few-shot classification by meta-learning" what to
observe" and" where to attend" in a relational perspective. Our method leverages relational …