Specifically, the ALSI model consists of two important modules, namely Scale ResNet and Residual Hybrid Attention Fusion (RHAF). First, the Scale ResNet module takes the Constant-Q transform feature as input to obtain relatively important frequency information. Next, RHAF takes t...
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...
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 ...
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...
1)Feature Extraction Module: 特征提取模块: 由一个卷积层和 5 个带有 ReLU 激活函数的残差块[38] 组成。 使用共享特征提取模块,并将它们输入到 MDD 对齐模块中。 MDD Alignment Module: MDD对齐模块: 图4。 MDD 对齐模块的图示。 特征 Ft+i 和 Ft 由卷积层组合并输入 MDRB 以预测采样参数 Θt+i 。
3D object detectionFeature fusion moduleMulti-modalitiesCross-modal transformer blockLidar and camera are essential sensors for environment perception in autonomous... B Zhang,Y Wang,C Zhang,... - 《Machine Vision & Applications》 被引量: 0发表: 2024年 PVAFN: Point-Voxel Attention Fusion Network...
In this paper, a multi-scale feature fusion module is introduced into the graph convolutional network model, and the high-resolution low-level feature information in the feature map is fused with the semantic information of the high-level feature, which greatly improves the model's recognition ...
Specifically, we put forward the important token election module (ITEM) which utilizes multi-headed self-attention mechanism in vision transformer to evaluate the importance of all tokens. This module will guide the model to select tokens which contain discriminative local information and global ...
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 ...
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...