Cross-Attention in Transformer Decoder Transformer论文中描述了Cross-Attention，但尚未给出此名称。Transformer decoder从完整的输入序列开始，但解码序列为空。交叉注意将信息从输入序列引入解码器层，以便它可以预测下一个输出序列标记。然后，解码器将令牌添加到输出序列中，并重复此自回归过程，直到生成EOS令牌。Cross-...
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In DETR, object queries directly interact with the image to- kens through cross-attention in transformer decoder. For 3D object detection, one intuitive way is to concatenate the im- age and point cloud tokens together for further interaction with object queries. H...
理想的情况下,我们希望将每一个像素都视作一个token,但是计算量巨大,受到CNN局部特征提取特性的启发,我们将CNN的局部卷积方法引入到了Transformer中,在每个单独的patch中逐像素的计算self-attetion,就是文中的Inner-Patch Self-Attention (IPSA),我们把一个局部当作一个注意范围,而不是整个画面。同时,Transformer可以...
In the decoder of the transformer model, we apply cross-attention between the "memory" (encoder outputs) and "targets" (decoder inputs). For this, in the TransformerDecoderLayer, we use src_mask as mask: https://github.com/joeynmt/joeynmt/blob/master/joeynmt/transformer_layers.py#L269 ...
实际上这样的计算复杂度降的更低了,连特征数都没有相互乘,原始 Attention / IPSA / CPSA 复杂度分别为: N为 patch 大小 每一个 Transformer 层由两个 IPSA 和一个 CPSA 组成,在第一个 IPSA 中可以采用绝对位置编码,公式如下: 上述式子只是表示位置编码的位置,实际上更细节的结构公式表达为: ...
Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image patches for Transformer after the tokenization of the image is vast(e.g., ViT), ...
in CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification Edit The Cross-Attention module is an attention module used in CrossViT for fusion of multi-scale features. The CLS token of the large branch (circle) serves as a query token to interact with the patch ...
U-Transformer overcomes the inability of U-Nets to model long-range contextual interactions and spatial dependencies, which are arguably crucial for accurate segmentation in challenging contexts. To this end, attention mechanisms are incorporated at two main levels: a self-attention module leverages ...
本文是FasterTransformer Decoding源码分析的第六篇,笔者试图去分析CrossAttention部分的代码实现和优化。由于CrossAttention和SelfAttention计算流程上类似,所以在实现上FasterTransformer使用了相同的底层Kernel函数,因此会有大量重复的概念和优化点,重复部分本文就不介绍了,所以在阅读本文前务必先浏览进击的Killua:FasterTransforme...