联合表征(Joint Representation)将多个模态的信息一起映射到一个统一的多模态向量空间,Joint结构注重捕捉多模态的互补性,融合多个输入模态x_1,x_2获得多模态表征x_m=f(x_1,...,x_n),进而使x_m完成某种预测任务。 Joint Representation Multimodal learning with deep boltzmann machines (NIPS 2012)提出将 deep ...
多模态表示学习 Multimodal Representation 单模态的表示学习负责将信息表示为计算机可以处理的数值向量或者进一步抽象为更高层的特征向量,而多模态表示学习是指通过利用多模态之间的互补性,剔除模态间的冗余性,从而学习到更好的特征表示。主要包括两大研究方向:联合表示(Joint Representations)和协同表示(Coordinated Representat...
Deep learningMulti-fusionSemantic integrationRecently, learning joint representation of multimodal data has received more and more attentions. Multimodal features are concept-level compositive features which are more effective than those single-doi:10.1007/978-3-319-70096-0_29Zepeng Gu...
1、联合joint 单模态的表示联合投影到多模态的联合表示 神经网络模型:通常使用最后或倒数第二个神经层作为单模态数据表示的一种形式,为了使用神经网络构建多模态表示,每个模态都从几个单独的神经层开始,然后是一个隐藏层,将模态投影到联合空间,然后联合多模态表示本身通过多个隐藏层或直接用于预测 ...
early fusion,也称为feature-based,基于特征。通常是在各模态特征被抽取后就进行融合,通常只是简单的连接他们的表征,也就是joint representation,直接连接多个向量。并使用融合后的数据进行模型训练,相比之后两种在训练上更为简单。 late fusion,也称为decision-based,基于决策的。该方法在各个模态做出决策后才进行融合,...
Recently, learning joint representation of multimodal data has received more and more attentions. Multimodal features are concept-level compositive feature... Z Gu,L Bo,T Yue,... - International Conference on Neural Information Processing 被引量: 2发表: 2017年 Sentiment analysis on a low-resource...
Joint representation: As mentioned earlier, this approach involves encoding both modalities into a shared high-dimensional space. Techniques like deep learning-based fusion methods can be used to learn optimal joint representations. Coordinated representation: Instead of fusing the modalities directly, this...
To address the co-learning challenge in multimodal machine learning models, several techniques have been proposed. One approach is to use joint representation learning methods, such as deep canonical correlation analysis (DCCA) or cross-modal deep metric learning (CDML), which aim to learn a shared...
摘要原文 Learning effective joint embedding for cross-modal data has always been a focus in the field of multimodal machine learning. We argue that during multimodal fusion, the generated multimodal embedding may be redundant, and the discriminative unimodal information may be ignored, which often int...
Therefore, they only consider high level interactions between modalities to find a joint representation for them. In this paper, we propose a multimodal deep learning framework (MDLCW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the...