(3)Multi-Scale Feature Fusion Strategy(多尺度融合策略) 三向信息流的结构为网络提供了大量的特征信息。但随之而来的问题便是:如何融合这些特征呢? 在图像分割领域,经常使用逐元素加法、逐元素乘法或拼接,以及 conv1x1 和 conv3x3 卷积操作来融合特征。而在最近的点云分割工作中,通常使用拼接,然后使用一个或多个...
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...
Specifically, a multi-scale feature fusion module that uses multi-scale convolution to separate the feature expression of multi-modal information and calculates the weight of each modal feature channel is proposed. We use the idea of multi-scale convolution and selection kernel to complete multi-...
termed RTS-Net. This architecture primarily consists of three modules: a Multiscale Feature Fusion Module (MFFM), a Coordinated Attention Detection Module (CADM), and a Real-time Feature Extraction Module (RFEM). Specifically, the MFFM enhances detection accuracy for small-scale objects by intensiv...
图像去雾学习笔记三:Multi-scale boosted dehazing network with dense feature fusion(CVPR2020) 1.标题:基于多尺度增强的密集特征融合去雾网络 2.概述 本文提出了一种基于U-Net架构的具有密集特征融合的多尺度增强去雾网络。该方法是基于两种原理设计的——boosting 和 error feedback,表明它们适用于去雾问题。通过...
Feature Fusion Module (LFFM), which integrates the pre- and post-change feature information, thus improving the network's ability to identify the changed regions. The MFPF-Net also contains a Multi-Scale Feature Aggregation Module (MSFA), which adaptively assigns weights to the information in ...
In this paper, we propose a three-branch hierarchical multi-scale feature fusion network structure termed as HiFuse, which can fuse multi-scale global and local features without destroying the respective modeling, thus improving the classification accuracy of various medical images. There are two key...
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...
Intra-stage Feature Fusion (IFF)和轴注意力比较像,沿着H轴和W轴做了pooling Dual Transformer Bridge 就是把四个不同维度的特征拉直concat,然后做完attention后再分开,以此做到跨stage的attention 其中channel aware和token aware如下 实验 ablation study
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion笔记和代码 本篇论文的主要创新点是SOS增强策略和密集特征融合,创新点均是从其他领域进行挖掘。 摘要 提出了一种基于U-Net结构的具有密集特征融合的多尺度增强去雾网络。 该方法基于增强反馈和误差反馈两种原理进行了设计,并证明了该方法适用于脱雾问题。