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- [2010. 11929] An Image is Worth 16x16 Words: Transformers for Image . . .
View a PDF of the paper titled An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, by Alexey Dosovitskiy and 10 other authors
- ViT开山之作解读：An Image is Worth 16x16 Words . . .
论文地址： An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 这篇论文由Google AI团队提出，是Vision Transformer（ViT）的开山之作，将 Transformer模型 成功应用于图像分类任务，证明了在大规模数据集上预训练的ViT可以超越传统的 卷积神经网络 （CNN）。
- 【论文精读】An Image is worth 16X16 words: transformer for . . .
"An Image is Worth 16x16 Words": 这个标题巧妙地概括了其核心方法。 它将一张图像分割成一系列固定大小的小块（patches），例如16x16像素。 每一个小块就被当作一个“单词”（word）。
- An Image is Worth 16x16 Words: Transformers for Image . . .
We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks
- ICLR2021 | ViT | 一张图像相当于 16×16 的词：大规模图像 . . .
本文 “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale” 提出将标准 Transformer 直接应用于 图像识别 任务的 Vision Transformer (ViT)，通过将图像分割为块并输入 Transformer 编码器，在大规模数据预训练后于多个图像识别基准测试中取得优异成绩，探讨了其预训练数据需求、模型缩放等特性，分析了内部机制并对自监督学习进行初步探索。
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
When pre-trained on large amounts of data and transferred to multiple recognition benchmarks (ImageNet, CIFAR-10, etc), these transformers attain excellent accuracy, matching or outperforming the best convolutional networks while requiring substantially less computational resources to train
- 论文总结与分析：“An Image is Worth 16x16 Words”-阿里云 . . .
本文介绍了ViT：视觉转换器的使用，而不是CNN或混合方法来执行图像任务。
- 论文解读：AN IMAGE IS WORTH 16X16 WORDS . . .
题目：An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 作者：谷歌大脑团队（Dosovitskiy, A , Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Untert…
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