2010 11929 An Image Is Worth 16x16 Words Transformers For

An Image is Worth 16x16 Words Transformers for Image Recognition at Scale Alexander Kolesnikov We show that this reliance on ConvNets is not necessary and a pure transformer can perform very well on image classification tasks when applied directly to sequences of image patches When pretrained on large amounts of data and transferred to

An Image is Worth 16x16 Words Transformers for Image ResearchGate

arXiv201011929v2 csCV 3 Jun 2021

An Image is Worth 16x16 Words Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly Jakob Uszkoreit Neil Houlsby

201011929 An Image is Worth 16x16 Words Transformers for Image

Paper page An Image is Worth 16x16 Words Transformers for Image

Abstract page for arXiv paper 201011929 An Image is Worth 16x16 Words Transformers for Image Recognition at Scale 201011929 cs Submitted on 22 Oct 2020 last revised 3 Jun 2021 this version v2 Title An Image is Worth 16x16 Words Transformers for Image Recognition at Scale

arxiv201011929 An Image is Worth 16x16 Words Transformers for Image Recognition at Scale Published on Oct 22 2020 Upvote 6 Authors Alexey Dosovitskiy ImageTexttoText Updated Sep 21 459k 257 googlevitbasepatch16224in21k Image Feature

2010 11929 An Image Is Worth 16x16 Words Transformers For

Corpus ID 225039882 An Image is Worth 16x16 Words Transformers for Image Recognition at Scale articleDosovitskiy2020AnII titleAn Image is Worth 16x16 Words Transformers for Image Recognition at Scale authorAlexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg

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An Image is Worth 16x16 Words Transformers for Image Recognition

An Image is Worth 16x16 Words Transformers for Image Recognition at

201011929 An Image is Worth 16x16 Words Transformers for Image Recognition at Scale

An Image is Worth 16x16 Words Transformers for Image Recognition at Scale While the Transformer architecture has become the defacto 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

2010 11929 An Image Is Worth 16x16 Words Transformers For

An Image is Worth 16x16 Words Transformers for Image Recognition at Scale

An Image is Worth 16x16 Words Transformers for Image ICLR

Download Citation An Image is Worth 16x16 Words Transformers for Image Recognition at Scale While the Transformer architecture has become the defacto standard for natural language processing

Published as a conference paper at ICLR 2021 AN IMAGE IS WORTH 16X16 WORDS TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE Alexey Dosovitskiyy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly Jakob Uszkoreit Neil Houlsbyy equal technical contributionyequal advising

An Image is Worth 16x16 Words Transformers for Image Recognition at Scale Dosovitskiy Alexey Beyer Lucas Kolesnikov Alexander 1048550arXiv201011929 arXiv arXiv201011929 Bibcode 2020arXiv201011929D Keywords Computer Science Computer Vision and Pattern Recognition

Selfattentionbased architectures in particular Transformers Vaswani et al 2017 have become the model of choice in natural language processing NLPThe dominant approach is to pretrain on a large text corpus and then finetune on a smaller taskspecific dataset Devlin et al 2019Thanks to Transformers computational efficiency and scalability it has become possible to train