  We present a High-Resolution Transformer (HRFormer) that learns
high-resolution representations for dense prediction tasks, in contrast to the
original Vision Transformer that produces low-resolution representations and
has high memory and computational cost. We take advantage of the
multi-resolution parallel design introduced in high-resolution convolutional
networks (HRNet), along with local-window self-attention that performs
self-attention over small non-overlapping image windows, for improving the
memory and computation efficiency. In addition, we introduce a convolution into
the FFN to exchange information across the disconnected image windows. We
demonstrate the effectiveness of the High-Resolution Transformer on both human
pose estimation and semantic segmentation tasks, e.g., HRFormer outperforms
Swin transformer by $1.3$ AP on COCO pose estimation with $50\%$ fewer
parameters and $30\%$ fewer FLOPs. Code is available at:
https://github.com/HRNet/HRFormer.
