At the transformer layer, a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities. Finally, the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer...
我的理解是multimodal指的就是visual words和text两种modal,所以他才说是multimodal的;至于你说的cross-modal我不是很清楚,不能随便乱说。 发布于 2013-05-06 20:24 赞同添加评论 分享收藏喜欢收起 吕阿华 浙江大学 计算机硕士 关注 17 人赞同了该回答 《Retrieving Multimoda...
在可替换的SA层之后,视觉LQ和文本LQ将通过独立的multi-head cross-attention(CA)层分别从预训练的视觉和文本特征中提取美学特征。与SA层不同,key和value是由预训练的视觉或文本特征构建的,可以表示为: 其中m表示v或t。权重矩阵W^Q_{mh}∈\mathbb{R}^{H_q×d},W^K_{vh}, W^V_{vh}∈\mathbb{R}^{...
The cross-modal attention aims to incorporate the correspondence between two volumes into the deep learning features for registering multi-modal images. To better bridge the modality difference between the MR and TRUS volumes in the extracted image features, we also introduce a novel contrastive ...
Multi-Modal Attention Network Learning for Semantic Source Code Retrieval,题目意思是用于语义源代码检索的多模态注意网络学习,2019年发表于ASE的 ## 研究什么东西 Background: 研究代码检索技术,对于一个代码存储库进行方法级别的搜索,给定一个描述代码片段功能的短文,从代码存储库中检索特定的代码片段。
Cross-attention(交叉注意力)是注意力机制的一种变体,用于在处理序列数据时,通过将不同部分之间的关联性引入到注意力机制中。通常,注意力机制关注输入序列中不同位置的信息,而交叉注意力则引入了多个序列之间的关联。在交叉注意力中,通常有两个输入序列(例如,源序列和目标序列),每个序列都有自己的查询(Query)、键(...
While Multi-Head Self-Attention (MH-SA) is added to the Bi-LSTM model to perform relation extraction, which can effectively avoid complex feature engineering in traditional tasks. In the process of image extraction, the channel attention module (CAM) and the spatial attention module (SAM) are ...
We conduct an exact theoretical analysis to demonstrate that multi-head attention with a substantial embedding dimension performs better than single-head attention. When the number of in-context examples D increases, the prediction loss using single-/multi-head attention is in O(1/D), and the ...
However, the modeling ability of single-head attention is weak. To address this problem,Vaswani et al. (2017)proposedmulti-head attention(MHA). The structure is shown inFig. 3(right). MHA can enhance the modeling ability of each attention layer without changing the number of parameters. ...
Therefore, in this paper, we propose multi-head attention fusion networks (MAFN) that use speech, text, and motion capture data such as facial expression, hand action, and head rotation to perform multi-modal speech emotion recognition. We begin by modeling the temporal sequence features of spee...