cross attention

Cross attention is a mechanism in artificial intelligence, specifically in transformer-based neural networks, that allows different parts of the input to attend to each other during the processing. It helps improve the accuracy of the model and has been widely used in natural language processing tasks such as machine translation and text summarization.

相关问题

cross Attention

Cross Attention是一种注意力机制，用于在两个不同的序列之间建立关联。它通常用于自然语言处理任务中，如机器翻译和文本摘要。

在机器翻译任务中，Cross Attention可以帮助模型将源语言句子中的每个单词与目标语言句子中的相关单词进行对齐，从而更好地进行翻译。

Cross Attention的计算过程如下：
1. 对于每个目标语言单词，计算其与源语言所有单词的相似度得分。
2. 将相似度得分进行softmax归一化，得到每个源语言单词对当前目标语言单词的注意力权重。
3. 将源语言单词的表示向量按照注意力权重进行加权平均，得到当前目标语言单词的上下文表示。

cross aTTENTION

Cross attention is a type of attention mechanism used in neural networks for natural language processing tasks such as machine translation, sentiment analysis, and question answering. It involves computing the attention weights between two different sets of input sequences, typically encoded as embeddings or representations. In machine translation, for example, cross attention is used to align the source and target language sequences, allowing the model to focus on the most relevant parts of each sequence during decoding.