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 …

Abstract Graph Convolutional Network (GCN) which models the potential relationship
between non-Euclidean spatial data has attracted researchers' attention in deep learning in …

Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have
dominated computer vision. However, recently proposed vision graph neural networks (ViG) …

Non-local operation is widely explored to model the long-range dependencies. However,
the redundant computation in this operation leads to a prohibitive complexity. In this paper …

The realm of graph-based modeling has proven its adaptability across diverse real-world
data types. However, its applicability to general computer vision tasks had been limited until …

Despite Graph Neural Networks demonstrating considerable promise in graph
representation learning tasks, GNNs predominantly face significant issues with over-fitting …

Convolutional neural networks (CNNs) have achieved great success on grid-like data such
as images, but face tremendous challenges in learning from more generic data such as …

We propose learning graph representations from 2D feature maps for visual recognition. Our
method draws inspiration from region based recognition, and learns to transform a 2D image …

Message passing neural networks (MPNNs) have become a dominant flavor of graph neural
networks (GNNs) in recent years. Yet, MPNNs come with notable limitations; namely, they …

Abstract Graph Neural Networks (GNNs) have achieved tremendous success in graph
representation learning. Unfortunately, current GNNs usually rely on loading the entire …