结点k在模态m下,经过Graph Sage层前后的embedding变化如下: 可以看到Graph Sage层对某个结点的更新就是不断将其邻接结点的信息融合进来,Wm是Graph Sage层本身的权重,Ws是共享权重,在第一个encoder里是一个可学习权重,在后面两个encoder里是一个全1的矩阵。 SAGPool层: 是一种分层池化的结构(类似MIL中的分层池化...
基于图神经网络的消息传递思想,我们设计了一个多模态图卷积网络(Multi-modal Graph Convolution Network,MMGCN)框架,该框架可以生成用户和微视频特定模态的表征,以更好地捕捉用户的偏好。具体地说,我们在每个模态上构造一个用户-项目二分图(bipartite graph),并用其邻接节点的拓扑结构和特征来丰富每个节点的表征。通过...
从负样本的角度来看,论文提出了一种扰动策略,以生成挑战性负样本,以充分探索模态之间的相关性,并确保每个模态在学习表征中的有效贡献。 参考文献:Multi-modal Graph Contrastive Learning for Micro-video Recommendation
在讨论子模块之前,我们首先介绍了两个关键组件:多模态知识图谱实体编码器(multi-modal knowledge graph entity encoder)和多模态知识图谱注意层(multi-modal knowledge graph attention layer),它们是KG嵌入模块和推荐模块的基本构建块。 •多模态知识图谱实体编码器,使用不同的编码器嵌入每种特定的数据类型。 •多模...
Then a multi-modal embedding enhancement mechanism, which consists of a multi-modal graph convolution network and an attention network, is developed to achieve cross-modal enhancement guided by the neighborhood structure and learn an effective joint embedding. Moreover, a joint loss based on contrast...
2.1.1 Multi-modal Graph 首先建造一个图, 按照以下步骤建立 (无向图) 1.建结点 在节点集V中,每个节点代表一个文本单词或一个视觉对象。 具体来说,我们采用以下策略来构造这两种节点: 我们将所有单词作为单独的文本节点包括在内,以便充分利用文本信息 ...
To this end, we propose an end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality. To effectively exploit the rich information across multi-modality associated with the disease, modality-aware representation learning is proposed to aggregate the features of...
Multi-modal Graph learning for Disease Prediction (IEEE Trans. on Medical imaging, TMI2022) License MIT license 93stars15forksBranchesTagsActivity Star Notifications main BranchesTags Code Folders and files Latest commit 66 Commits MMGL_inductive ...
UMGF: Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance This repository contains the source code for the paper: UMGF: Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance Install python3.7 transformers==3.4.0 torch==1.7.1 pytorch-crf...
we propose a multi-modal graph attention technique to conduct information propagation over MMKGs, and then use the resulting aggregated embedding representation for recommendation. To the best of our knowledge, this is the first work that incorporates multi-modal knowledge graph into recommender systems...