在翻译上,“Spatial Attention”通常被译为“空间注意力”,这一翻译直接且准确地传达了其核心含义。 另一个与通道注意力密切相关的术语是“Attention Mechanism”(注意力机制)。作为深度学习中的一项关键技术,注意力机制通过模拟人类视觉系统的选择性关注能力,显著提升了模型的性能...
compress(x)x_out = self.spatial(x_compress)scale = torch.sigmoid_(x_out)return x * scaleclass TripletAttention(nn.Module):def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):super(TripletAttention, self).__init__()self.ChannelGateH ...
Channel Attention方面,大致结构还是和SE相似,不过作者提出AvgPool和MaxPool有不同的表示效果,所以作者对原来的特征在Spatial维度分别进行了AvgPool和MaxPool,然后用SE的结构提取channel attention,注意这里是参数共享的,然后将两个特征相加后做归一化,就得到了注意力矩阵。 Spatial Attention和Channel Attention类似,先在cha...
所提出的Triplet Attention见下图所示。顾名思义,Triplet Attention由3个平行的Branch组成,其中两个负责捕获通道C和空间H或W之间的跨维交互。最后一个Branch类似于CBAM,用于构建Spatial Attention。最终3个Branch的输出使用平均进行聚合。 1、Cross-Dimension Interaction 传统的计算通道注意力的方法涉及计算一个权值,然后使...
题目:SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning 作者: Long Chen等(浙大、新国立、山大) 期刊:CVPR 2017 1 背景 注意力机制已经在自然语言
Spatial-Channel Atention(SCA): spatial attention block:采用pyramid scales,序列使用7*7,5*5,3*3卷积。通过逐层上采样实现不同尺度特征的结合获得精确的多尺度信息。并且采用global pooling提供全局context information。使用channel-wise attention map 实现特征的通道选择。上图b显示了channel-wise attention fusion ...
Channel & spatial attention combines the advantages of channel attention and spatial attention. It adaptively selects both important objects and regions
空间-通道注意力。第二种类型称为SpatialChannel(S-C),是一种首先实现空间注意的模型。对于S-C型,在给定初始特征图V的情况下,我们首先利用空间注意力Φ来获得空间注意力权重α。基于α、线性函数fs(·)和通道方向注意模型Φc,我们可以按照C-S类型的配方来计算调制特征X: ...
In this paper, we propose a deep object co-segmentation method based on channel and spatial attention, which combines the attention mechanism with a deep neural network to enhance the common semantic information. Siamese encoder and decoder structure are used for this task. Firstly, the encoder ...
Furthermore, we construct our own spatial module with respect to the self-attention mechanism, which not only captures long-distance spatial connections, but also provides more stability for feature extraction. Experimental results demonstrate that our attention-based network improves the performance of ...