vit 的encoder中的Multi-Head self-attention
时间: 2024-05-24 11:15:23 浏览: 75
在ViT模型的Encoder中,Multi-Head self-attention是一个非常重要的组成部分。它主要用于提取输入序列中各个位置之间的语义依赖关系,同时也有助于对输入进行特征提取和降维。
具体来说,Multi-Head self-attention将输入序列划分为多个头(通常是8或16个),每个头都会计算出一个注意力权重矩阵,用于表示每个位置与其他位置之间的相关程度。这些头的计算结果会被拼接在一起,进一步被一个线性变换映射到一个低维空间中,最终得到一个表示输入序列的特征向量。
通过使用Multi-Head self-attention,ViT模型能够同时考虑输入序列中的不同位置和语义信息,从而实现更加准确和细粒度的特征提取。
相关问题
Local-to-Global Self-Attention in Vision Transformers
Vision Transformers (ViT) have shown remarkable performance in various vision tasks, such as image classification and object detection. However, the self-attention mechanism in ViT has a quadratic complexity with respect to the input sequence length, which limits its application to large-scale images.
To address this issue, researchers have proposed a novel technique called Local-to-Global Self-Attention (LGSA), which reduces the computational complexity of the self-attention operation in ViT while maintaining its performance. LGSA divides the input image into local patches and performs self-attention within each patch. Then, it aggregates the information from different patches through a global self-attention mechanism.
The local self-attention operation only considers the interactions among the pixels within a patch, which significantly reduces the computational complexity. Moreover, the global self-attention mechanism captures the long-range dependencies among the patches and ensures that the model can capture the context information from the entire image.
LGSA has been shown to outperform the standard ViT on various image classification benchmarks, including ImageNet and CIFAR-100. Additionally, LGSA can be easily incorporated into existing ViT architectures without introducing significant changes.
In summary, LGSA addresses the computational complexity issue of self-attention in ViT, making it more effective for large-scale image recognition tasks.
vit encoder中的mlp
在ViT(Vision Transformer)中,MLP(Multilayer Perceptron)是编码器中的一个关键组件。它被用来处理每个位置的特征向量,以便更好地捕捉局部和全局特征。
具体来说,MLP由两个线性变换和一个非线性激活函数组成,通常是GELU或ReLU。第一个线性变换将输入特征向量映射到一个更高维度的空间,第二个线性变换将其映射回原始维度。这样做的目的是增加特征的复杂度并学习更高级别的特征表示。
在ViT中,MLP被用来增强特征的表达能力,使其更好地适应任务要求。