Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant
role in achieving competitive performance. In this paper, we further explore the capacity of …

Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …

This paper presents ClusterFormer, a universal vision model that is based on the Clustering
paradigm with TransFormer. It comprises two novel designs: 1) recurrent cross-attention …

Generative AI (GenAI) has witnessed remarkable progress in recent years and
demonstrated impressive performance in various generation tasks in different domains such …

With the springing up of face synthesis techniques, it is prominent in need to develop
powerful face forgery detection methods due to security concerns. Some existing methods …

This paper studies how to keep a vision backbone effective while removing token mixers in
its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are …

What is an image and how to extract latent features? Convolutional Networks (ConvNets)
consider an image as organized pixels in a rectangular shape and extract features via …

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

Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …