  Transformers are quickly becoming one of the most heavily applied deep
learning architectures across modalities, domains, and tasks. In vision, on top
of ongoing efforts into plain transformers, hierarchical transformers have also
gained significant attention, thanks to their performance and easy integration
into existing frameworks. These models typically employ localized attention
mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin
Transformer's Shifted Window Self Attention. While effective at reducing self
attention's quadratic complexity, local attention weakens two of the most
desirable properties of self attention: long range inter-dependency modeling,
and global receptive field. In this paper, we introduce Dilated Neighborhood
Attention (DiNA), a natural, flexible and efficient extension to NA that can
capture more global context and expand receptive fields exponentially at no
additional cost. NA's local attention and DiNA's sparse global attention
complement each other, and therefore we introduce Dilated Neighborhood
Attention Transformer (DiNAT), a new hierarchical vision transformer built upon
both. DiNAT variants enjoy significant improvements over strong baselines such
as NAT, Swin, and ConvNeXt. Our large model is faster and ahead of its Swin
counterpart by 1.6% box AP in COCO object detection, 1.4% mask AP in COCO
instance segmentation, and 1.4% mIoU in ADE20K semantic segmentation. Paired
with new frameworks, our large variant is the new state of the art panoptic
segmentation model on COCO (58.5 PQ) and ADE20K (49.4 PQ), and instance
segmentation model on Cityscapes (45.1 AP) and ADE20K (35.4 AP) (no extra
data). It also matches the state of the art specialized semantic segmentation
models on ADE20K (58.1 mIoU), and ranks second on Cityscapes (84.5 mIoU) (no
extra data).
