In this work, we propose a novel FD model by integrating multi-scale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiGRU), and cross self-attention feature fusion (CSAFF). We have developed innovative designs in two modules, namely MQCNN and CSAFF. ...
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 ICML 2024重磅!GeminiFusion:高效逐像素多模态融合!引领Vision Transformer新纪元! GeminiFusion: Efficient Pixel-wise Multimodal...
Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing - mubai011/MQCCAF
2.ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection 方法: 作者提出了一种新颖的双交叉注意力特征融合方法,用于多光谱目标检测,同时聚合了RGB和热红外图像的互补信息。 该方法包括三个阶段:单模态特征提取、双模态特征融合和检测。在单模态特征提取阶段,分别对RGB和热红外...
Consequently, we propose a novel and effective feature fusion mechanism named Mutual-Cross-Attention (MCA). Combining with a specially customized 3D Convolutional Neural Network (3D-CNN), this purely mathematical mechanism adeptly discovers the complementary relationship between time-domain and frequency-...
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, ...
Zhang, Y., Han, S., Zhang, Z., et al.: CF-GAN: cross-domain feature fusion generative adversarial network for text-to-image synthesis. Visual Comput. 39(4), 1283–1293 (2022) Google Scholar Peng, D., Yang, W., Liu, C., et al.: SAM-GAN: self-attention supporting multi-stage...
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,...
有效的原因就是在 Self-Attention 模块中两个模态进行了融合操作: 摘录一段其中的 Transformer 模块代码以供参考: defforward(self,x):rgb_fea=x[0]# rgb_fea (tensor): dim:(B, C, H, W)ir_fea=x[1]# ir_fea (tensor): dim:(B, C, H, W)assertrgb_fea.shape[0]==ir_fea.shape[0]bs,...