Open AccessProceedings Article
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 +11 more
TLDR
The Vision Transformer (ViT) as discussed by the authors uses a pure transformer applied directly to sequences of image patches to perform very well on image classification tasks, achieving state-of-the-art results on ImageNet, CIFAR-100, VTAB, etc.Abstract:
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 applied directly to sequences of image patches can perform very well on image classification tasks. 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.read more
Citations
More filters
Posted Content
Open-Set Recognition: A Good Closed-Set Classifier is All You Need
TL;DR: This article showed that the ability of a classifier to make the "none-of-above" decision is highly correlated with its accuracy on the closed-set classes, and further demonstrated the trend both on the standard OSR benchmarks as well as on a large-scale ImageNet evaluation.
Posted Content
Non-deep Networks
TL;DR: In this paper, the authors use parallel subnetworks instead of stacking one layer after another, which helps effectively reduce depth while maintaining high performance and achieves state-of-the-art performance.
Posted Content
On the Expressive Power of Self-Attention Matrices.
TL;DR: In this paper, the authors show that the self-attention matrix can approximate any sparse matrix up to a given precision defined in terms of preserving matrix element ratios, where sparsity is defined as a bounded number of nonzero elements in each row and column.
Posted Content
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation.
Srinadh Bhojanapalli,Ayan Chakrabarti,Himanshu Jain,Sanjiv Kumar,Michal Lukasik,Andreas Veit +5 more
TL;DR: In this article, the authors investigate the global structure of attention scores computed using this dot product mechanism on a typical distribution of inputs, and study the principal components of their variation through eigen analysis of full attention score matrices.
Posted Content
TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection
TL;DR: Wang et al. as discussed by the authors proposed a pure remote photoplethysmography transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently, which is lightweight and efficient (with only 547k parameters and 763M FLOPs).
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.