本文的出发点在于作者所提出的基于self-attention机制的模块的缺陷,即:由于self-attention机制缺乏inductive bias(归纳偏置,何为归纳偏执在本篇笔记的后续会介绍,可以理解为一种先验信息),所以,基于self-attention机制的方法,需要很大的语料才能训练出一个泛化能力较好的模型。于是,作者提出来一种inductive bias:基于self-...
2).reshape(B,N,C//2)attn2=(q[:,self.num_heads//2:]@k2.transpose(-2,-1))*self.scaleattn2=attn2.softmax(dim=-1)attn2=self.attn_drop(attn2)v2=v2+self.local_conv2(v2.transpose(1,2).reshape(B,-1,C//2).transpose(1,2).view(B,C//2,H*2//self.sr_ratio...
Therefore, we propose a multi-scale self-attention generative adversarial network (MSSA GAN) to restore colon tissue pathological images. The MSSA GAN uses a self-attention mechanism in the generator to efficiently learn the correlations between the corrupted and uncorrupted areas at multiple scales....
"'Multi-scale self-guided attention for medical image segmentation'", which has been recently accepted at the Journal of Biomedical And Health Informatics (JBHI). Abstract Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have s...
为了解决这个问题,作者提出了一个新颖且通用的策略—shunted self-attention(SSA)。SSA 的关键思想是将异构感受野大小注入标记:在计算自注意力矩阵之前,它选择性地合并标记以表示更大的对象特征,同时保持某些标记以保留细粒度特征。这种新颖的合并方案使 self-attention 能够学习不同大小的对象之间的关系,同时降低令牌...
将宽度压缩为1self.pool_w = nn.AdaptiveAvgPool2d((1,None))# 自适应平均池化,保留宽度维度,将高度压缩为1self.gn = nn.GroupNorm(channels // self.groups, channels // self.groups)# 分组归一化self.conv1x1 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=1, ...
self-attentionmultiscale featurelarge displacementsocclusionsEven though optical flow approaches based on convolutional neural networks have achieved remarkable performance with respect to both accuracy and efficiency, large displacements and motion occlusions remain challenges for most existing learning-based ...
())# 结合高度和宽度特征,应用分组归一化x2 =self.conv3x3(group_x)# 对重构后的张量应用3x3卷积x11 =self.softmax(self.agp(x1).reshape(b *self.groups, -1,1).permute(0,2,1))# 对 x1 进行自适应平均池化并应用Softmaxx12 = x2.reshape(b *self.groups, c //self.groups, -1)# 重构 x2...
例如,Self-Attention只关注图像内部的相互关系,忽略了跨空间相关性;Channel Attention只考虑通道的权重分配,无法捕捉到细粒度的空间信息;Spatial Attention只通过人工规则对不同区域进行加权,缺乏自适应性。因此,这些传统方法在面对复杂场景下的多尺度特征提取和整合任务时存在一定的局限性。 4.2 现有的多尺度模型及其不足...
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