A General Framework for Auxiliary Feature: 作者认为辅助特征不仅对同一个位置上的主要特征造成影响,还会对周围邻居的主要特征造成影响,比如上游节点发生拥堵将会降低下游节点的速度,所以对主要特征与辅助特征的空间关系进行建模十分重要。其次作者认为辅助特征里也存在时空关联,所以使用了两个分割的时空模块分别提取主要特...
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...
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...
(2) Depth image pixels corresponding to the object are projected to generate the object's frustum point cloud, and a multi-modal feature fusion strategy simplifies the object's frustum point cloud, so as to remove outlier points and reduce the number of point clouds. This can replace the 3D...
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 。
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 ...
Multi-modal feature fusion: This module splices the extracted spatial features and temporal features to obtain MSTF and outputs them to the next module. Classification: This module uses a better performing SVM as a classifier, first inputting MSTF for training, and then inputting the test set in...
To fully interact with the feature encoding extracted from different paths, we propose a novel multi-feature fusion module (MFF) that can tightly link the feature Experimental validation In this section, we first introduce the dataset and describe the implementation details and the experimental setup...
Feature fusion module In order to obtain the final output hM of two BERT parts, we combine the output hkmer1 from the first scale input format layer and hkmer2 from the second scale input format layer through a dimensional-wise fusion gate F. F is accomplished by the sigmoid activation fu...
为了克服这些障碍,论文提出了一个基于行为的框架,称为多视角特征融合网络(Multi-view Feature Fusion Network, MFFN)。该框架模拟了人类在图像中寻找模糊物体的行为,即从多个角度、距离、视角进行观察。它背后的关键思想是通过数据增强生成多种观察方式(多视角),并将其应用于输入。