HRFormer adapts the transformer architecture for dense computer vision tasks by using multi-resolution parallel design inspired by HRNet, combined with local-window self-attention for computational efficiency. The model performs human pose estimation and semantic segmentation by processing image features at multiple resolutions simultaneously, addressing the memory and computation limitations of standard Vision Transformers that produce only low-resolution outputs.