4.4.2 RAAT( relation augmented attention transformer) 为了有效地编码实体和句子相关性,我们设计了RAAT,它利用了表示相关性的可计算矩阵,并将其集成到注意力计算中。根据图3所示的架构,RAAT继承自原始的transformer,但有一个独特的注意力计算模块,该模块由两部分组成:自我注意力和关系增强注意力计算
Although previous works have shown great success, they pay too much attention to the text modality while ignoring other important visual information, and the correlations between objects and text are not fully exploited. Moreover, traditional transformer-based architectures ignore global information ...
Image captioningPseudo-regionDynamic memoryCross-modal attention fusionTransformerIntroduce a Dual Relation Transformer (DRTran) model for image captioning.Design dual relation enhancement encoder to complement the advantages of grid and pseudo-region features.Devise dynamic memory module to learn prior ...
Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. This method assumes that the model can automatically learn linguistic knowledge (e.g., grammar and syntax) from the parallel corpus via an attention network. However, the attention ...
Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. This method assumes that the model can automatically learn linguistic knowledge (e.g., grammar and syntax) from the parallel corpus via an attention network. However, the attention net...