  The hybrid deep models of Vision Transformer (ViT) and Convolution Neural
Network (CNN) have emerged as a powerful class of backbones for vision tasks.
Scaling up the input resolution of such hybrid backbones naturally strengthes
model capacity, but inevitably suffers from heavy computational cost that
scales quadratically. Instead, we present a new hybrid backbone with
HIgh-Resolution Inputs (namely HIRI-ViT), that upgrades prevalent four-stage
ViT to five-stage ViT tailored for high-resolution inputs. HIRI-ViT is built
upon the seminal idea of decomposing the typical CNN operations into two
parallel CNN branches in a cost-efficient manner. One high-resolution branch
directly takes primary high-resolution features as inputs, but uses less
convolution operations. The other low-resolution branch first performs
down-sampling and then utilizes more convolution operations over such
low-resolution features. Experiments on both recognition task (ImageNet-1K
dataset) and dense prediction tasks (COCO and ADE20K datasets) demonstrate the
superiority of HIRI-ViT. More remarkably, under comparable computational cost
($\sim$5.0 GFLOPs), HIRI-ViT achieves to-date the best published Top-1 accuracy
of 84.3% on ImageNet with 448$\times$448 inputs, which absolutely improves
83.4% of iFormer-S by 0.9% with 224$\times$224 inputs.
