  We present the Group Propagation Vision Transformer (GPViT): a novel
nonhierarchical (i.e. non-pyramidal) transformer model designed for general
visual recognition with high-resolution features. High-resolution features (or
tokens) are a natural fit for tasks that involve perceiving fine-grained
details such as detection and segmentation, but exchanging global information
between these features is expensive in memory and computation because of the
way self-attention scales. We provide a highly efficient alternative Group
Propagation Block (GP Block) to exchange global information. In each GP Block,
features are first grouped together by a fixed number of learnable group
tokens; we then perform Group Propagation where global information is exchanged
between the grouped features; finally, global information in the updated
grouped features is returned back to the image features through a transformer
decoder. We evaluate GPViT on a variety of visual recognition tasks including
image classification, semantic segmentation, object detection, and instance
segmentation. Our method achieves significant performance gains over previous
works across all tasks, especially on tasks that require highresolution
outputs, for example, our GPViT-L3 outperforms Swin Transformer-B by 2.0 mIoU
on ADE20K semantic segmentation with only half as many parameters. Project
page: chenhongyiyang.com/projects/GPViT/GPViT
