Our global context attention module receives a global feature correlation map to elicit the contextual information from a given scene and generates the channel and spatial attention weights to modulate the target embedding to focus on the relevant feature channels and spatial parts of the target ...
Therefore, we introduce a global contextual attention module (GC)34, as shown in Fig. 3a. The GC module consists of context modeling, a bottleneck transform, and broadcast element-wise addition. Figure 3 Illustration of the backbone with global contextual attention: (a) global contextual ...
1、global context(全局上下文信息):如 PSPNet 和 Deeplab v3 的金字塔池化和看空洞金字塔池化。但为了得到较好的效果,空洞卷积下采样了8次,费时费内存【16】 2、attention module(注意力模块):帮助我们只关注想要的信息,可以关注不同尺度的信息。本文用了 channel attention。【16,17】 3、refinement residual bloc...
Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine...
这是一篇在显著目标检测中引用比较高的一篇文章,主要创新性的提出一种模型网络架构,叫做全局感知渐进聚合网络(GCPANet: Global Context-Aware Progressive Aggregation Network),用以有效整合低层次外观特征、高层次语义特征和全局感知特征。 模型骨架网络采用 ResNet-50 作为编码器,之后作者提出由多个模块组合的解码器用来...
因此local attention与global attention的唯一差异在于query的来源,前者来自模块输入,而后者则来自于stage初生成,内部各个module共享的global token。 整个stage的流程如下: 特征送如一个stage中,即GCViTLayer 首先根据送入的特征计算global query。这里的计算中,会将输入特征下采样到和后续计算的window-bas...
MICN: Multi-scale Local and Global Context Modeling for Long-term...openreview.net/forum?id=zt53IDUR1U 本文中了2023 ICLR的oral。又是一篇长时间序列预测的文章,但是它是一个基于时域卷积模块的模型,而不是基于Transformer的模型。本文的动机有两点:先提取时间序列的局部特征,然后再提取所有局部特征之间的...
显著性目标检测之Global Context-Aware Progressive Aggregation Network for Salient Object Detection 三层的卷积结构。没啥好说的。HA由于编码器组件的顶层特征对于显著目标检测通常是冗余的,我们设计了一个接在顶层后的HA模块,通过利用空间和通道注意机制来学习更具有选择性和代表性的特征。 输入特征图...Context-Aware...
[20] combined pyramid feature fusion and global context attention to construct a PGA-Net for surface defect detection. However, their attention is a cross-layer feature fusion module, which cannot realize the feature interaction of a certain layer. Yang et al. [21] constructed an end-to-end ...
Global pooling之后可以得到chanel维度数量的特征向量，与一般的pooling完接激活层再接全连接层相比，可以...