Criss-Cross Attention & Axial Attention 都是基于Self-Attention 的改版。 Self-Attention 先从Self-Attention开始,理论学习看这一篇修仙:Self-Attention学习,代码参考self-attention代码_深度菜鸡-达闻西的博客-CSDN博客_selfattention代码 classSelf_Attn(nn.Module):""" Self attention Layer"""def__init__(self,...
torchvision.datasets.CIFAR10: CIFAR10数据集的PyTorch实现,用于进行图像分类任务。 步骤2:定义crisscross注意力机制的网络模型结构 在这一步中,我们将定义crisscross注意力机制的网络模型结构。这里我们以一个简单的卷积神经网络为例。下面是代码示例: classCrissCrossAttention(nn.Module):def__init__(self):super(Cr...
我们看个例子: The animal didn't cross the street because it was too tired 这里的it到底代表的是animal还是street呢,对于我们来说能很简单的判断出来,但是对于机器来说,是很难判断的,self-attention就能够让机器把it和animal联系起来,接下来我们
第三步:yolo.py中注册 CrissCrossAttention模块 elif m is CrissCrossAttention: c1, c2 = ch[f], args[0] if c2 != no: c2 = make_divisible(c2 * gw, 8) args = [c1, *args[1:]] 第四步:修改yaml文件,本文以修改head(特征融合网络)为例,将原C3模块后加入该模块。 backbone: # [from, num...
1、GPU memory friendly. Compared with the non-local block, the recurrent criss-cross attention module requires 11× less GPU memory usage.阡陌注意力模块与使用non-local模块比,GPU内存减少11倍。 2、High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about ...
reduction。 经过recurrent criss-cross attention (RCCA)模块: 第一个CC注意力模块,使每个像素都汇集其十字路线上的语义信息。 第二个CC注意力模块,每个像素获得了所有点的语义信息。(这两个CC注意力模块是共享参数的,)。 拼接H''和X,经过多个卷积、BN、激活函数得到分割图。Criss-Cross AttentionA...
3.2. Criss-Cross Attention image.png 通过Affinity操作产生A,操作定义如下: 3.3. Recurrent Criss-Cross Attention 尽管交叉注意模块可以在水平和垂直方向上捕获长距离上下文信息,但是像素和周围像素之间的连接仍然是稀疏的。获取语义分割的密集上下文信息是有帮助的。为实现这一目标,我们基于上述交叉注意模块引入了循环交...
Recurrent criss-cross attention module can be unrolled into R=2 loops, in which all Criss-Cross Attention modules share parameters.Visualization of the attention mapTo get a deeper understanding of our RCCA, we visualize the learned attention masks as shown in the figure. For each input image,...
CCNet: Criss-Cross Attention for Semantic SegmentationZilong Huang 1∗ , Xinggang Wang 1 , Lichao Huang 2 , Chang Huang 2 , Yunchao Wei 3 , Wenyu Liu 11 School of EIC, Huazhong University of Science and Technology2 Horizon Robotics3 Beckman Institute, University of Illinois at Urbana-...
Then I check the gradient, the theoretical gradient of z is 1. Gradient of CC() is excatly 1, but gradient of CUDA CrissCross() is 0.9999998212. As for the speed of tranning and testing, I compare my Pytorch Criss-Cross Attention and the official CUDA Criss-Cross Attention in this proje...