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 Fusion for Vision Transformer 作者列表: Ding Jia, Jianyuan Guo, Kai Han,Han Wu, Chao Zhang, Chang Xu, Xinghao ...
设计了由不同像素强度约束组成的分割像素损失函数,用于训练ATFuse,以便在融合结果中实现纹理细节和亮度信息的良好权衡。 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...
A novel dual cross-attention feature fusion method is proposed for multispectral object detection, which simultaneously aggregates complementary information from RGB and thermal images.An iterative learning strategy is tailored for efficient multispectral feature fusion, which further improves the model perform...
Multi-Scale Feature Fusion 为了让两个分支的数据可以进行融合交互,提出了多种方案 All-Attention: 直接两个分支拿过来一起计算注意力【计算开销大】 Class Token Fusion:只是用 Class Token 进行混合(直接使用加法) Pairwise Fusion:基于 patch 所属的空间位置进行混合——这里会先进行插值来对其空间大小,然后再进行...
Based on this, this paper proposes a method of multi-cross attention feature fusion. First, DistilBioBERT and CharCNN and CharLSTM are used to perform cross-attention word-char (word features and character features) fusion separately. Then, the two feature vectors obtain...
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, ...
Multi-Scale Feature Fusion 为了让两个分支的数据可以进行融合交互,提出了多种方案 All-Attention: 直接两个分支拿过来一起计算注意力【计算开销大】 Class Token Fusion:只是用 Class Token 进行混合(直接使用加法) Pairwise Fusion:基于 patch 所属的空间位置进行混合——这里会先进行插值来对其空间大小,然后再进行...
Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing - mubai011/MQCCAF
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...
Multi-Scale Feature Fusion 有效的特征融合是学习多尺度特征表示的关键。作者探索了四种不同的方法融合解决策略:三种简单的启发式方法和所提出的交叉注意模块,如图3所示。 (a)全注意融合,将两个branch的token concatenate起来,而不考虑任何token的特征。 (b)类标token融合,class token可以视为是一个branch的全局特征...