如下图所示,此文所提出的模型由四部分构成,分别为 dynamic graph constructor, primary block, auxiliary block 和 multifaceted fusion module. 其中 primary block 和 auxiliary block 由temporal convolution 和 dynamic graph conv组成。其中四个部分堆叠L层并且进行skip connection。 Dynamic Graph Construction: 路段间...
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...
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...
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...
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-...
A novel, multi-modal feature fusion based framework is prosed to obtain an effective representation for each superpixel annotation. The framework consists of four sequential modules (Fig. 2): 1) a double-channel (including both shallow and deep modality) based, low-level feature extraction; 2...
In order to better solve this problem, a new camouflage target segmentation method based on multi-level feature fusion is proposed. A multi-stage gate control module is introduced to selectively fuse the multi-stage middle layer features of Res2Net-50, which can eff...
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...
MF2ResU-Net: A Multi-Feature Fusion Deep Learning Architecture for Retinal Blood Vessel Segmentation 来自 Semantic Scholar 喜欢 0 阅读量: 184 作者:Z Cui,SJ Song,L Chen,J Qi 摘要: Segmentation of blood vessels becomes an essential step in computer aided diagnosis system for the diseases in ...
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。