Modern vision transformers leverage visually inspired local interaction
between pixels through attention computed within window or grid regions, in
contrast to the global attention employed in the original ViT. Regional
attention restricts pixel interactions within specific regions, while sparse
attention disperses them across sparse grids. These differing approaches pose a
challenge between maintaining hierarchical relationships vs. capturing a global
context. In this study, drawing inspiration from atrous convolution, we propose
Atrous Attention, a blend of regional and sparse attention that dynamically
integrates both local and global information while preserving hierarchical
structures. Based on this, we introduce a versatile, hybrid vision transformer
backbone called ACC-ViT, tailored for standard vision tasks. Our compact model
achieves approximately 84% accuracy on ImageNet-1K with fewer than 28.5 million
parameters, outperforming the state-of-the-art MaxViT by 0.42% while requiring
8.4% fewer parameters.

本研究提出了一种新的混合视觉 transformer 模型 (ACC-ViT)，运用区域关注和稀疏关注相结合的方式，动态地集成了局部和全局信息，同时保留了分层结构，并在常见的视觉任务中表现出色。

视觉 Transformer 中的区域与稀疏注意力融合

Fusion of regional and sparse attention in Vision Transformers

Human gesture recognition has drawn much attention in the area of computer
vision. However, the performance of gesture recognition is always influenced by
some gesture-irrelevant factors like the background and the clothes of
performers. Therefore, focusing on the regions of hand/arm is important to the
gesture recognition. Meanwhile, a more adaptive architecture-searched network
structure can also perform better than the block-fixed ones like Resnet since
it increases the diversity of features in different stages of the network
better. In this paper, we propose a regional attention with
architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace
the fixed Inception modules with the automatically rebuilt structure through
the network via Neural Architecture Search (NAS), owing to the different shape
and representation ability of features in the early, middle, and late stage of
the network. It enables the network to capture different levels of feature
representations at different layers more adaptively. Meanwhile, we also design
a stackable regional attention module called dynamic-static Attention (DSA),
which derives a Gaussian guidance heatmap and dynamic motion map to highlight
the hand/arm regions and the motion information in the spatial and temporal
domains, respectively. Extensive experiments on two recent large-scale RGB-D
gesture datasets validate the effectiveness of the proposed method and show it
outperforms state-of-the-art methods. The codes of our method are available at:
this https URL

本文使用神经结构搜索技术，并结合自适应的网络结构及局部注意力，提出了一种针对手势识别的 RAAR3DNet 方法，该方法有效地提取了手臂区域以及运动信息，并在两个大型 RGB-D 手势数据集上超越了现有技术。