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-...
https://vaclavkosar.com/ml/cross-attention-in-transformer-architecture 交叉注意力与自我注意力 除了输入,cross-attention 计算与self-attention相同。交叉注意力不对称地组合了两个相同维度的独立嵌入序列,相比之下,自注意力输入是一个单一的嵌入序列。其中一个序列用作查询输入,而另一个用作键和值输入。SelfDoc ...
This study explores cutting-edge vision transformer architecture to revolutionize feature extraction in the context of duplicate image identification. Our proposed framework combines the conventional transformer architecture with a ground-breaking cross-attention layer developed specifically for this study. This...
In 2017, the transformer architecture introduced a standalone self-attention mechanism, eliminating the need for RNNs altogether. (For brevity, and to keep the article focused on the technical self-attention details, I am keeping this background motivation section brief so that we can focus on ...
1. Introduction The novel transformer architecture [36] has led to a big leap forward in capabilities for sequence-to-sequence mod- eling in NLP tasks [10]. The great success of transform- ers in NLP has sparked particular interest from the vision ...
In this paper, we propose a novel transformer encoder-decoder architecture for 3D human mesh reconstruction from a single image, called FastMETRO. We identify the performance bottleneck in the encoder-based transformers is caused by the token design which introduces high complexity interactions among ...
Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or using local attention windows. In contrast, we propose a different approach that aims to improve the performance of transformer...
augustwester/transformer-xl Star35 Code Issues Pull requests A lightweight PyTorch implementation of the Transformer-XL architecture proposed by Dai et al. (2019) nlppytorchtransformerself-attentionxlnettransformer-xlcross-attention UpdatedFeb 7, 2023 ...
(CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, ...
a transformer architecture for optical flow (springer, tel aviv, israel, 2022), pp.668–685 h. wu, j. wu, j. xu, j. wang, m. long, flowformer: linearizing transformers with conservation flows. arxiv preprint arxiv:2202.06258 (2022) r. caruana, multitask learning. mach. learn. 28 (...