Local Vision Transformers

Local Vision Transformers（LVT）是一种基于Transformer架构的图像分类模型。与传统的卷积神经网络（CNN）相比，LVT采用了自注意力机制来捕捉图像中的全局和局部信息。

LVT将输入图像分割成多个局部区域，并将每个区域作为一个独立的图像块输入到Transformer模型中。每个图像块通过多个自注意力层进行特征提取和交互，然后将得到的特征进行池化和全连接层处理，最后进行分类。

相比于传统的CNN模型，LVT具有以下优势：
1. 全局和局部信息的建模：通过自注意力机制，LVT能够同时捕捉到图像的全局和局部信息，从而更好地理解图像内容。
2. 灵活性：LVT可以根据输入图像的大小和复杂度进行动态调整，适应不同尺寸和分辨率的图像。
3. 可解释性：由于Transformer模型的结构简单明了，LVT能够提供更好的可解释性，帮助理解模型的决策过程。

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.

focal self-attention for local-global interactions in vision transformers

Focal Self-Attention for Local-Global Interactions in Vision Transformers是指在视觉转换器中使用聚焦自我注意力机制来实现局部和全局交互的技术。这种技术可以帮助模型更好地理解图像中的局部和全局信息，从而提高视觉任务的性能。