Transformers have elevated to the state-of-the-art vision architectures
through innovations in attention mechanism inspired from visual perception. At
present two classes of attentions prevail in vision transformers, regional and
sparse attention. The former bounds the pixel interactions within a region; the
latter spreads them across sparse grids. The opposing natures of them have
resulted in a dilemma between either preserving hierarchical relation or
attaining a global context. In this work, taking inspiration from atrous
convolution, we introduce Atrous Attention, a fusion of regional and sparse
attention, which can adaptively consolidate both local and global information,
while maintaining hierarchical relations. As a further tribute to atrous
convolution, we redesign the ubiquitous inverted residual convolution blocks
with atrous convolution. Finally, we propose a generalized, hybrid vision
transformer backbone, named ACC-ViT, following conventional practices for
standard vision tasks. Our tiny version model achieves $\sim 84 \%$ accuracy on
ImageNet-1K, with less than $28.5$ million parameters, which is $0.42\%$
improvement over state-of-the-art MaxViT while having $8.4\%$ less parameters.
In addition, we have investigated the efficacy of ACC-ViT backbone under
different evaluation settings, such as finetuning, linear probing, and
zero-shot learning on tasks involving medical image analysis, object detection,
and language-image contrastive learning. ACC-ViT is therefore a strong vision
backbone, which is also competitive in mobile-scale versions, ideal for niche
applications with small datasets.

通过从视觉感知中汲取灵感进行注意机制创新，Transformer 已经成为最先进的视觉架构。本文引入了一种融合区域和稀疏注意力的 Atrous Attention，它能够自适应地整合局部和全局信息，并保持层次关系，提出了一种通用的混合式视觉 Transformer 骨干网络 ACC-ViT，适用于标准视觉任务和移动规模版本，适用于具有小数据集的特定应用领域。