transformer shifted

在Transformer模型中，右移是指在解码器端输入序列时将目标序列右移一个位置。这是因为在训练过程中，解码器需要在每个时间步预测下一个单词，所以为了保持一致性，我们将目标序列右移一个位置。这样，解码器在每个时间步可以使用先前预测的单词作为上下文信息来生成下一个单词。这个右移操作确保了解码器在训练和推理时具有相同的输入和输出序列长度。

相关问题

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Swin Transformer is a type of hierarchical vision transformer that uses shifted windows to improve the efficiency of processing images. The traditional vision transformer processes images by dividing them into smaller patches, which are then fed into a transformer network. However, this approach can be computationally expensive, as the number of patches can be quite large for high-resolution images.

Swin Transformer addresses this issue by using a hierarchical approach, where the image is first divided into larger patches. These patches are then processed by a smaller transformer network, which produces feature maps that are used to further divide the image into smaller patches. This process is repeated multiple times, with each stage processing smaller and smaller patches to produce increasingly detailed feature maps.

In addition to this hierarchical approach, Swin Transformer also uses shifted windows to further reduce the number of patches that need to be processed. Rather than dividing the image into regular patches, the windows are shifted by a certain amount, leading to overlapping patches. This approach reduces the number of patches needed to represent the image, while still maintaining the ability to capture spatial information.

Overall, Swin Transformer has shown promising results on image classification tasks, achieving state-of-the-art performance on several benchmarks while requiring less computational resources than previous approaches.

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows精读

Swin Transformer是一种新型的层次化视觉Transformer模型，它在Vision Transformer（ViT）的基础上进行了改进，并在多个视觉任务上取得了更好的效果。本文将对Swin Transformer论文进行精读，详细介绍其创新点和实验结果。

## 创新点

Swin Transformer主要有以下三个创新点：

### 1. 层次化注意力

Swin Transformer引入了层次化注意力机制，将图像分成多个块进行处理，每个块内部使用全局自注意力机制，不同块之间使用局部注意力机制。这种层次化的注意力机制可以减少全局自注意力机制的计算量，同时保持局部信息的传递。

### 2. Shifted Window

传统的ViT使用固定大小的图像块进行处理，而Swin Transformer使用了一种称为Shifted Window的方法，将每个块按照一定的步长进行平移，使得每个块都包含了周边的信息。这种方法可以更好地捕捉到图像中的全局信息。

### 3. Swin Transformer Block

Swin Transformer引入了一个新的Swin Transformer Block，它是由多个Shifted Window构成的，每个Shifted Window内部使用了类似于ViT的注意力机制。这种新的Transformer Block可以更好地捕捉到局部和全局的信息。

## 实验结果

Swin Transformer在多个视觉任务上都取得了很好的效果，比如ImageNet分类、COCO目标检测、Cityscapes语义分割等。在ImageNet上，Swin Transformer比ViT-Large模型具有更好的性能，同时参数数量更少，计算效率更高。在COCO目标检测任务中，Swin Transformer在使用相同的backbone的情况下，比ViT-Large模型具有更高的AP值。在Cityscapes语义分割任务中，Swin Transformer在使用相同的backbone的情况下，比DeiT-base模型具有更高的mIoU值。

## 总结

Swin Transformer是一种新的层次化视觉Transformer模型，它引入了层次化注意力机制、Shifted Window和Swin Transformer Block等创新点，并在多个视觉任务上取得了很好的效果。这些创新点可以更好地捕捉到图像中的局部和全局信息，同时减少了计算量，提高了计算效率。