  Recently, Vision Transformer and its variants have shown great promise on
various computer vision tasks. The ability of capturing short- and long-range
visual dependencies through self-attention is arguably the main source for the
success. But it also brings challenges due to quadratic computational overhead,
especially for the high-resolution vision tasks (e.g., object detection). In
this paper, we present focal self-attention, a new mechanism that incorporates
both fine-grained local and coarse-grained global interactions. Using this new
mechanism, each token attends the closest surrounding tokens at fine
granularity but the tokens far away at coarse granularity, and thus can capture
both short- and long-range visual dependencies efficiently and effectively.
With focal self-attention, we propose a new variant of Vision Transformer
models, called Focal Transformer, which achieves superior performance over the
state-of-the-art vision Transformers on a range of public image classification
and object detection benchmarks. In particular, our Focal Transformer models
with a moderate size of 51.1M and a larger size of 89.8M achieve 83.5 and 83.8
Top-1 accuracy, respectively, on ImageNet classification at 224x224 resolution.
Using Focal Transformers as the backbones, we obtain consistent and substantial
improvements over the current state-of-the-art Swin Transformers for 6
different object detection methods trained with standard 1x and 3x schedules.
Our largest Focal Transformer yields 58.7/58.9 box mAPs and 50.9/51.3 mask mAPs
on COCO mini-val/test-dev, and 55.4 mIoU on ADE20K for semantic segmentation,
creating new SoTA on three of the most challenging computer vision tasks.
