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如何用 Transformer 一步一步改进 Unet? | 集智书童
https://www.chaucerg.top/2021/09/27/18/
本文提出了一种用于医学图像分析的基于Transformer和UNet的神经网络,Transformer直接处理原始图像而不是提取的特征图,性能优于Trans-Unet等网络。作者单位:北京Zoezen机器人,北航 简介 医学图像分割在生物医学图像分析中占有重要地位,也引起了人们的广泛关注。良好的分割结果可以帮助医生进行判断,进一步改善患者体验。 在医学图像分析的众多可用方法中,UNet是最受
85FPS!CNN+Transformer 语义分割的又一境界,真的很快! | 集智书童
https://www.chaucerg.top/2021/09/23/17/
本文提出了一种用于城市场景语义分割的高效混合Transformer(EHT),其利用CNN和Transformer结合学习全局-局部上下文来加强特征表征,性能优于ABCNet等网络,速度高达83.4FPS!代码将开源!作者单位:武汉大学,兰卡斯特大学等 简介高分辨率城市场景图像的语义分割在土地覆盖制图、城市变化检测、环境保护和经济评估等广泛的实际应用中起着至关重要的作用。卷积神经网络采用分
CompConv 卷积让模型不丢精度还可以提速 | 集智书童
为了降低CNN的计算成本,本文提出了一种新的卷积设计:它利用分治法策略来简化特征图的转换。即插即用!可直接替换普通卷积,几乎不牺牲性能,极致压缩CNN结构!
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