In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective
vision transformer architecture that is able to capture global context while maintaining …

Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …

Although convolutional networks have been the dominant architecture for vision tasks for
many years, recent experiments have shown that Transformer-based models, most notably …

We propose focal modulation networks (FocalNets in short), where self-attention (SA) is
completely replaced by a focal modulation module for modeling token interactions in vision …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Transformers have quickly shined in the computer vision world since the emergence of
Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) …

Abstract Recently, visual Transformer (ViT) and its following works abandon the convolution
and exploit the self-attention operation, attaining a comparable or even higher accuracy than …

Transformer 所具备的长距离建模能力和并行计算能力使其在自然语言处理领域取得了巨大
成功并逐步拓展至计算机视觉等领域. 本文以分类任务为切入, 介绍了典型视觉Transformer …

In the field of computer vision, recent works show that a pure MLP architecture mainly
stacked by fully-connected layers can achieve competing performance with CNN and …

We propose a novel few-shot action recognition framework, STRM, which enhances class-
specific feature discriminability while simultaneously learning higher-order temporal …