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
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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
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Attention Mechanisms in Computer Vision: A Survey.
Meng-Hao Guo,Tian-Xing Xu,Jiangjiang Liu,Zheng-Ning Liu,Peng-Tao Jiang,Tai-Jiang Mu,Song-Hai Zhang,Ralph R. Martin,Ming-Ming Cheng,Shi-Min Hu +9 more
TL;DR: A comprehensive review of attention mechanisms in computer vision can be found in this article, which categorizes them according to approach, such as channel attention, spatial attention, temporal attention and branch attention.
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Incorporating Convolution Designs into Visual Transformers.
TL;DR: CeiT as discussed by the authors combines the advantages of CNNs in extracting low-level features, strengthening locality, and the advantage of Transformers in establishing long-range dependencies, which can reduce the training cost significantly.
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Jieneng Chen,Yongyi Lu,Qihang Yu,Xiangde Luo,Ehsan Adeli,Yan Wang,Le Lu,Alan L. Yuille,Yuyin Zhou +8 more
TL;DR: TransUNet as mentioned in this paper combines Transformers and U-Net to enhance finer details by recovering localized spatial information for medical image segmentation, which achieves superior performances to various competing methods on different medical applications including multi-organ segmentation and cardiac segmentation.
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MLP-Mixer: An all-MLP Architecture for Vision
Ilya Tolstikhin,Neil Houlsby,Alexander Kolesnikov,Lucas Beyer,Xiaohua Zhai,Thomas Unterthiner,Jessica Yung,Andreas Steiner,Daniel Keysers,Jakob Uszkoreit,Mario Lucic,Alexey Dosovitskiy +11 more
TL;DR: MLP-Mixer as discussed by the authors is an architecture based exclusively on multi-layer perceptrons (MLP), which contains two types of layers: one with MLPs applied independently to image patches (i.e. "mixing" the per-location features), and one with LSTM applied across patches, and it achieves competitive scores on image classification benchmarks, with pre-training and inference cost comparable to state-of-theart models.
Proceedings ArticleDOI
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
TL;DR: The Wikipedia-based Image Text (WIT) dataset as mentioned in this paper is a curated set of 37.5 million entity rich image-text examples with 11.5 millions unique images across 108 Wikipedia languages.
References
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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.