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.

一种基于高分辨率输入的新型混合主干网络 (HIRI-ViT)，通过将典型的卷积神经网络操作分解为两个并行的卷积神经网络分支来构建，一个直接以高分辨率特征为输入，但使用更少的卷积操作，另一个首先进行下采样，然后在低分辨率特征上使用更多的卷积操作，通过对 ImageNet、COCO 和 ADE20K 数据集的实验表明了 HIRI-ViT 的优越性。

HIRI-ViT：高分辨率输入下的视觉 Transformer 尺度扩展

HIRI-ViT: Scaling Vision Transformer with High Resolution Inputs

Multi-contrast (MC) Magnetic Resonance Imaging (MRI) reconstruction aims to
incorporate a reference image of auxiliary modality to guide the reconstruction
process of the target modality. Known MC reconstruction methods perform well
with a fully sampled reference image, but usually exhibit inferior performance,
compared to single-contrast (SC) methods, when the reference image is missing
or of low quality. To address this issue, we propose DuDoUniNeXt, a unified
dual-domain MRI reconstruction network that can accommodate to scenarios
involving absent, low-quality, and high-quality reference images. DuDoUniNeXt
adopts a hybrid backbone that combines CNN and ViT, enabling specific
adjustment of image domain and k-space reconstruction. Specifically, an
adaptive coarse-to-fine feature fusion module (AdaC2F) is devised to
dynamically process the information from reference images of varying qualities.
Besides, a partially shared shallow feature extractor (PaSS) is proposed, which
uses shared and distinct parameters to handle consistent and discrepancy
information among contrasts. Experimental results demonstrate that the proposed
model surpasses state-of-the-art SC and MC models significantly. Ablation
studies show the effectiveness of the proposed hybrid backbone, AdaC2F, PaSS,
and the dual-domain unified learning scheme.

提出了一种新的双域核磁共振成像重建网络 DuDoUniNeXt，采用混合主干网络结构，自适应粗到精特征融合模块（AdaC2F）和部分共享浅层特征提取器（PaSS）来处理不同对比度图像的一致性和差异性信息，实验证明该模型明显优于现有的单对比度和多对比度模型。