HRFormer: High-Resolution Transformer for Dense Prediction

• URL: http://arxiv.org/abs/2110.09408v2
• Date: Thu, 21 Oct 2021 05:53:13 GMT
• Title: HRFormer: High-Resolution Transformer for Dense Prediction
• Authors: Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, Jingdong Wang
• Abstract summary: We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks. We take advantage of the multi-resolution parallel design introduced in high-resolution convolutional networks (HRNet) We demonstrate the effectiveness of the High-Resolution Transformer on both human pose estimation and semantic segmentation tasks.
• Score: 99.6060997466614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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