  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.
