In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …

Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …

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 …

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 …

Occluded person re-identification (ReID) aims to match occluded person images to holistic
ones across dis-joint cameras. In this paper, we propose a novel framework by learning high …

Abstract 3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made great …

Abstract Graph Neural Networks (GNNs) with attention have been successfully applied for
learning visual feature matching. However, current methods learn with complete graphs …

Matching local features across images is a fundamental problem in computer vision.
Targeting towards high accuracy and efficiency, we propose Seeded Graph Matching …

The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

For the traveling salesman problem (TSP), the existing supervised learning based
algorithms suffer seriously from the lack of generalization ability. To overcome this …