我们可以利用Cross Attention构建强大的骨干,生成不同尺度的特征图,满足下游任务不同粒度特征的需求,如图1所示。我们在不增加计算量或少量增加计算量的情况下引入全局关注,这是一种更合理的结合Transformer和CNN特征的方法。 Transformer和CNN的功能是相辅相成的,我们的长期目标是将它们更有效、更完美地结合起来,以充分...
我觉得本文提出的跨 patch 间的 Attention 很有趣,其实说实话整体变化很简单,就是在 Attention 的阶段 permute 维度顺序就可以了。标题图是作者对当今结构的一个总结,(a) 为 CNN 的层级结构,逐渐降采样;(b) 为传统 ViT 结构,特征大小始终保持不变;(c) 本文提出在 Transformer 中也逐层降采样。 层级结构其实...
Arxiv 2106 - CAT: Cross Attention in Vision Transformer 论文:https://arxiv.org/abs/2106.05786 代码:https://github.com/linhezheng19/CAT 详细解读:https://mp.weixin.qq.com/s/VJCDAo94Uo_OtflSHRc1AQ 核心动机:使用patch内部和patch之间attention简化了...
提出了一种双分支transformer分别提取不同尺度特征以及基于cross attention的融合机制融合不同branch的特征,这种机制是线性复杂度的。在Flops和参数没有大很多的情况下,比DeiT高两个点,和EfficientNet相比comparable. 动机 ViT with a patch size of 16 outperforms the ViT with a patch size of 32 by 6% but the...
CAT: Cross Attention in Vision Transformer 来自 Semantic Scholar 喜欢 0 阅读量: 474 作者:H Lin,X Cheng,X Wu,F Yang,W Yuan 摘要: Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the ...
论文地址:[2108.00154] CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention (arxiv.org) 代码地址:https://github.com/cheerss/CrossFormer 一、Motivation 主要还是ViT的历史遗留问题 ViT在处理输入时,将图片划分为了相等大小的图像块(Patch),然后通过linear操作生成token序列,这种操作导致Vi...
写在前面:最近再看transformer系列论文,以此做个记录。 介绍的是CAT:Cross Attention in Vision Transformer 论文地址:CAT:Cross Attention in Vision Transformer 代码地址:https://github.com/linhezheng19/CAT 同期论文如Swin Transformer和Pyramid Vis... ...
This is official implement of"CAT: Cross Attention in Vision Transformer". Abstract Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image pa...
The objective of this paper is to propose a novel approach to brain tumor classification using a Vision Transformer (ViT) with a novel cross-attention mechanism. Our approach leverages the strengths of transformers in modeling long-range dependencies and multi-scale feature fusion. We introduce two...
通常来说,标准的 Transformer 包括 6 个编码器和 6 个解码器串行。 1. 编码器内部接收源翻译输入序列,通过自注意力模块提取必备特征,通过前向网络对特征进行进一步抽象。 2. 解码器端输入包括两个部分,一个是目标翻译序列经过自注意力模块提取的特征,一个是编码器提取的全局特征,这两个输入特征向量会进行交叉注意...