B. Dual Feature Fusion Module ①步骤: (1)在获得噪声信息提供的注意图后,我们将该注意图与空间流的输入相乘,得到一个新的特征图. (2)根据通道维度将新的特征图X‘rgb与原始特征图拼接到rgb流中。然后,我们使用1×1卷积层获得一个结合RGB和噪声信息的特征融合。 (3)在获得融合特征Xfusion后,我们对其进行...
Figure 1 shows that the tracking results of our AMFT tracking method are similar to the ground-truth labels of the tracking target, demonstrating the effectiveness of the proposed multi-feature fusion model. Fig. 1 Tracking examples. AMFT_H represents the tracking results with only hand-crafted ...
explicit motion compensation The fTAN includes three modules: feature extraction module, Multi-scale Dilated Deformable (MDD) alignment module and attention module. 特征提取模块、多尺度扩张变形(MDD)对齐模块和注意力模块。 1)Feature Extraction Module: 特征提取模块: 由一个卷积层和 5 个带有 ReLU 激活函...
classCAMV(nn.Module):def__init__(self,in_dim,mm_size):super().__init__()self.conv_l_pre_down=ConvBNReLU(in_dim,in_dim,5,stride=1,padding=2)self.conv_l_post_down=ConvBNReLU(in_dim,in_dim,3,1,1)self.conv_m=nn.Sequential(ConvBNReLU(in_dim,in_dim,3,1,1),ConvBNReLU(in...
In the feature cross fusion module, the number of heads for cross attention is 8. In the classification, the number of convolution kernels in the CNN layer is 64, the kernel size is 3, and the dropout ratio is 0.5. The number of neurons in the linear layers decreases layer by layer ...
Handcrafted features extracted based on the prior knowledge and hidden deep features are complementarily fused through the feature fusion module, and then the hybrid features are fed into the multiplicative long short-term memory (MLSTM) to explore the temporal dependency in EEG signals. A one-...
The multi-modal feature fusion (MFF) module fuses the features extracted by SFE and TFE in parallel into MSTF to obtain more comprehensive feature information. A Light ResNet is designed based on the idea of residuals and depth-separable convolution. Compared to the traditional ResNet18, its ...
本文提出Multi-Level Feature Pyramid Network来搭建高效检测不同尺度目标的特征金字塔。MLFPN由FFM、TUMs以及SFAM三部分组成。其中FFMv1(Feature Fusion Module)用于混合由backbone提取的多层级特征作为基础特征;TUMs(Thinned U-shape Modules)以及FFMv2s通过基础特征提取出多层级多尺度的特征;SFAM(Scale-wise Feature Aggr...
MLFPN包含交替连接的Thinned U-shape Modules(TUM)、Feature Fusion Module(FFM)和Scale-wise Feature Aggregation Module (SFAM)。其中,TUMs和FFMs提取出更具代表性的多级多尺度特征。(值得注意的是,每个U-shape Module中的decoder层具有相似的深度。)SFAM最后利用scale-wise拼接和channel-wise attention来聚合收集具有...
Finally, we propose an object counting algorithm based on a feature extraction backbone, a feature fusion module and a density map regression head, called... Y Wang,B Yang,X Wang,... - 《Neural Networks the Official Journal of the International Neural Network Society》 被引量: 0发表: 2024...