最近师妹问我spatial attention和channel attention的问题,我查了一下,网上一堆资料讲的云里雾里的,这里对个人理解做一些笔记。这两种注意力机制结构如下: 注意力机制,其实就是模仿人关注Region of Interest的过程(可参考显著图Saliency map)。 接下来以RGB图片 I ( H , W ) I_{(H, W)} I(H,W)为简...
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...
gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):super(TripletAttention, self).__init__()self.ChannelGateH = SpatialGate()self.ChannelGateW = SpatialGate()self.no_spatial=no_spatialif not no_
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...
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 ...
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
Channel attentionSpatial attentionDeep learningObject co-segmentation is a challenging task, which aims to segment common objects in multiple images at the same time. Generally, common information of the same object needs to be found to solve this problem. For various scenarios, common objects in ...
Channel attention module (CAM). Full size image Figure 4 Spatial attention module (SAM). Full size image The SAM generates spatial attention maps\({\text{M}}_{\text{s}}\left({\text{F}}\right)\), which are used to emphasize or suppress features in different spatial locations. First, ...
We propose a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training ... S Yan,S Zhang,X He - 《Proceedings of the Aaai Confer...
We propose a Grad-CAM guided channel-spatial attention module for the FGVC, which employs the Grad-CAM to supervise and constrain the attention weights by generating the coarse localization maps. To demonstrate the effectiveness of the proposed method, we conduct comprehensive experiments on three ...