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Image recognition (i.e. classifying what object is shown in an image) is a core task in computer vision, as it enables various downstream applications (automatically tagging photos, assisting visually impaired people, etc.), and has become a standard task on which to benchmark machine learning (ML) algorithms. Deep learning (DL) algorithms have, over the past decade, emerged as the most competitiv
Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for l
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 nece
論文紹介 / An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
第六回 全日本コンピュータビジョン勉強会 Transformer論文読み会 にて、 "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" [Dosovitskiy et al., ICLR 2021] のご紹介をさせていただきました。 ◆イベント詳細 URL: https://kantocv.connpass.com/event/205271/ ◆発表日: 2021/04/18 ◆紹介論文の Open Review の URL: https://openreview.net/forum?id=YicbFdNTTy
はじめに 前回は店舗における画像認識の活用事例を見てきましたが、今回は駅や空港、駐車場などの交通機関および地域まるごと顔認証決済という取り組みなどを紹介します。現代の知識習得は動画活用がポイントですので、ベンダー各社が制作した動画も紹介しています。ぜひ、どこまで実現できているかを映像で確かめてみてください。 交通機関における画像認識 1. 電車 私のような切符世代にとっては駅の自動改札でさえ夢のような産物なのですが、さらに進化した「顔パス改札」が始まっています。進んでいるのはやはり中国で、2019年頃から深セン、成都、太原、鄭州、広州、南寧、昆明、西安、ハルピン、貴陽、福州など各都市の地下鉄で顔認証改札が続々導入されています。スマホで顔を登録して顔パス認証する様子を四川省の成都地下鉄の動画でご覧ください。 私のようにモバイルSuicaのタッチで満足している人も多いでしょうが、中国がやるなら
Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness for image recognition. We consider two forms of self-attention. One is pairwise self-attention, which generalizes standard dot-product attention and is fundamentally a set operator. The other is patchwise self-attention,
‘Farewell Convolutions’ – ML Community Applauds Anonymous ICLR 2021 Paper That Uses Transformers for Image Recognition at Scale | Synced
‘Farewell Convolutions’ – ML Community Applauds Anonymous ICLR 2021 Paper That Uses Transformers for Image Recognition at Scale ICLR 2021 paper An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale suggests Transformers can outperform top CNNs on CV at scale. A new research paper, An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale, has the machine learn
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A Visual History of Interpretation for Image Recognition
High-Performance Large-Scale Image Recognition Without Normalization
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
AIは実社会でどのように活用されているのか④ー画像認識(2)(Image Recognition)
Exploring Self-attention for Image Recognition
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