Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing - mubai011/MQCCAF
Multi-scale feature fusion: Self-attention: 3. Methodology 在本节中,我们首先粗略地展示网络结构,并描述它们如何完成分类和分割任务。然后,我们介绍了如何使用self-attention mechanism构建CSA模块。最后,我们详细描述了如何构建多尺度融合(MF)模块。 3.1. Overview 给定一个包含N个点的点云集合。每个点有3个坐标和...
【双模型 mask 自监督】MST: Masked Self-Supervised Transformer for Visual Representation 煎饼果子不...发表于自监督学习 论文笔记:Attention is all you need(Transformer) 今天做作业没 【学习笔记】Transformer,Attention和Multi Head Attention 决定对自己负责起来从把基础打好开始,学习使我快乐。 Attention is Al...
The second attention module is the gated cross-attention feature fusion module (GC-FFM) which combines interaction features for semantic prediction. We design a gated cross-attention mechanism to automatically adjust the fusion weight of cross-modal information in cross-attention by introducing a gated...
论文阅读06——《CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering》 Ideas: Model: 交叉注意力融合模块 图自编码器 Ideas: 提出一种基于端到端的交叉注意力融合的深度聚类框架,其中交叉注意力融合模块创造性地将图卷积自编码器模块和自编码器模块多层级连起来 ...
In this paper, we propose a novel feature fusion framework of dual cross-attention transformers to model global feature interaction and capture complementary information across modalities simultaneously. In addition, we introdece an iterative interaction mechanism into dual cross-attention transformers, ...
The representative one is deep cross-modal hashing (DCMH) [9], which uses a deep neural network to simultaneously learn feature representations and hash codes in an end-to-end Proposed SAALDH The problem definition, the details of our proposed self-attention and adversary learning deep hashing ...
In the feature fusion part, we design a cross-modal attention fusion module, which can leverage the attention mechanism to fuse multi-modality and multi-level features. In the feature decoding part, we design a progressive decoder to gradually fuse low-level features and filter noise information ...
提出一种新的基于交叉注意力机制(Cross Attention Mechanism, CAM)的红外和可见光图像融合方法,称为CrossFuse。这种方法旨在增强互补信息(uncorrelation),减少冗余特征,以生成包含更多互补信息且较少冗余特征的融合图像。 创新点 交叉注意力机制(CAM):提出了一种新的交叉注意力机制,该机制通过自注意力(self-attention)...
如上图所示,在BEVFormer中,多幅图像首先经过主干网络进行特征提取,然后输入空间交叉注意力模块(Spatial Cross-Attention)转换为BEV特征。为了降低计算量,BEVFormer中采用了可变形注意力(Deformable Attention)来实现交叉注意力的计算。 在一般的自注意力计算中,我们需要定义query,key和value。假设元素个数为N,那么query,...