An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale  | Papers With Code

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer can perform very well on image classification tasks when applied directly to sequences of image patches. When pre-trained on large amounts of data and transferred to multiple recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc), Vision Transformer attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

虽然Transformer 已经成为NPL任务的标准配置,它在计算机视觉领域的应用依然是有限的。在计算机视觉领域,注意力机制可以与卷积网络一起使用,也可以用于替换网络中指定的部分而不改变整体网络结构。我们在本文中展示了一种不使用CNNs而是使用全transformer的架构,这种架构可以通过输入一系列的图像patch完成图像分类任务。


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