[2010.11929] An Image is Worth 16x16 Words: Transformers for …
2020年10月22日 · When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
Vision Transformer Explained - Papers With Code
The Vision Transformer, or ViT, is a model for image classification that employs a Transformer-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard Transformer encoder.
NielsRogge/Vision-Transformer-papers - GitHub
This repository contains an overview of important follow-up works based on the original Vision Transformer (ViT) by Google. - NielsRogge/Vision-Transformer-papers
A Survey on Vision Transformer | IEEE Journals & Magazine - IEEE …
2022年2月18日 · In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages. The main categories we explore include the backbone network, high/mid-level vision, low …
Vision Transformers: A Review of Architecture ... - IEEE Xplore
In this paper, we present an outline of the main concepts and components of vision transformers. We review various variations and modifications to the architecture, and compare different approaches based on their effectiveness, complexity, and other attributes.
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 ...
Vision Transformers: State of the Art and Research Challenges
2022年7月7日 · This paper presents a comprehensive overview of the literature on different architecture designs and training tricks (including self-supervised learning) for vision transformers. Our goal is to provide a systematic review with the open research opportunities.
An Overview of Vision Transformers | Papers With Code
Vision Transformers are Transformer-like models applied to visual tasks. They stem from the work of ViT which directly applied a Transformer architecture on non-overlapping medium-sized image patches for image classification.
To address this, we scale ViT models and data, both up and down, and character-ize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption …
To mitigate these issues, we propose a novel deformable self-attention module, where the positions of key and value pairs in self-attention are selected in a data-dependent way. This flexible scheme enables the self-attention module to focus on relevant re-gions and capture more informative features.
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