(1)encoder:改进的VGG-16-(1)通道数减半(2)在conv-4 layer之后cut the network (2)使用Spatial-Channel Attention module 提取multi-scale和global context features 来encode local 和global information。SCA具有空间和通道注意性,能够保证空间和通道特征的recalibrating。因此可以有效的区分特征并抑制不明显的特征。
Moreover, how to focus on key facial regions to improve the performance of HR estimation is also a challenging problem.To overcome this issue, this paper proposes a novel Spatial-Channel Mixed Attention Module (SCAM) to select the facial regions with high correlation for HR estimation adaptively...
A novel attention mechanism is proposed, namely the fused spatial channel attention, which is used to take the place of Squeeze-and-Excitation (SE) module in the original MobileNet v3 model. Results from experiments show that the presented technique performs better than the state-of-the-arts. ...
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 ...
1、SENet(Squeeze and Excite module) 2、CBAM(Convolutional Block Attention Module) 3、BAM(Bottleneck Attention Module) 4、Grad-CAM 5、Grad-CAM++ 6、-Nets(Double Attention Networks) 7、NL(Non-Local blocks) 8、GSoP-Net(Global Second order Pooling Networks) ...
In this paper, to accomplish this goal, it proposes to combine the channel attention and spatial attention module (C-SAM), the C-SAM can mine deeply more effective information using samples of different classes that exist in different tasks. The residual network is used to alleviate the loss ...
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, ...
The main contribution of this paper is the multi-kernel-size, spatial-channel attention method (MKSC) to analyze chest X-ray images for COVID-19 detection. Our proposed method integrates a feature extraction module, a multi-kernel-size attention module, and a classification module. We use X-...
We concatenate the filtered and original search region’s features, and send it to the channel attention module. This approach is used to achieve spatial channel attention. This enhances the representation of fusion features and makes them more discriminative. (2) We design a ranking head network...
ARM重复多次(Module Stacking),通过堆叠来加深网络,增强特征提取能力。 3.通道注意力(Channel Attention) 通道注意力机制专注于不同颜色通道的特征,通过Cx1xW(Channel-wise 1x1 Convolutions)操作来实现,这允许模型在通道维度上进行特征的加权。 4.空间注意力(Spatial Attention) ...