Papers › HRFormer: High-Resolution Transformer for Dense Prediction

HRFormer: High-Resolution Transformer for Dense Prediction

18 Oct 2021arXiv:2110.09408archive 2025-07-28

Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang

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.

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