整体的模型架构: 通过ResNet,LSTM和一个Embedding层处理图片,Question和Answer的输入。 并且通过一个Attention得到融合了QA信息的视觉特征Va和Vq。 通过一组dual Mutan模块进行特征融合。 并且通过LSTM和一个Linear的分类器得到最后的输出。 Mutan 来源于[1705.06676] MUTAN: Multimodal Tucker Fusion for Visual Question ...
MDAL: Multi-task Dual Attention LSTM Model for Semi-supervised Network EmbeddingDual attentionNetwork embeddingMulti-task learningIn recent years, both the academic and commercial communities have paid great attentions on embedding methods to analyze all kinds of network data. Despite of the great ...
,用模型去计算。 从后文的4.2 Hyperparameters里看,这里用来计算序列概率的模型是另外训练的一个三层LSTM,并且在Dual Training中保持参数不变。(体现在代码中就是先都计算好了存入dataloader) L att 另外作者为了引入CS和CG模型的对称性,认为代码(x)中的词与注释(y)中的词之间的对齐关系具有对称性,可以通过attenti...
Therefore, we propose a novel model called M ulti-task L earning based D ual B idirectional LSTM M odel (MLDBM) for ABSA of drug reviews. The MLDBM leverages BERT and incorporates a multi-head self-attention mechanism to produce aspect-specific representations which are further processed ...
Yan et al. [4] utilized a CNN-based model and a Long Short-Term Memory (LSTM) for robust breast cancer classification. Chen et al. [5] developed two attention blocks (spatial and channel) to address the challenge of classifying live cancer. Fu et al. [6] proposed a hybrid model that...
adecoderto predict the answer with a linear transform or to generate the question sequence with an LSTM, GRU. animage modelwhich can be a pretrained VGG16 or ResNet-152, optionally, anattention schemewhich may have several "glimpses".