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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SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
TL;DR: Wang et al. as discussed by the authors proposed an end-to-end transformer-based model for video captioning, which takes video frame patches directly as inputs, and outputs a natural language description.
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An Image is Worth 16x16 Words, What is a Video Worth?
TL;DR: In this paper, a temporal transformer is used to apply global attention over video frames, and thus better exploits the salient information in each frame, achieving state-of-the-art results on the Kinetics dataset.
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Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
TL;DR: Li et al. as mentioned in this paper proposed sMLPNet, which replaces the self-attention module in the token-mixing step with a novel sparse MLP (sMLP) module.
Book ChapterDOI
Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers.
Yingda Xia,Jiawen Yao,Le Lu,Lingyun Huang,Guotong Xie,Jing Xiao,Alan L. Yuille,Kai Cao,Ling Zhang +8 more
TL;DR: In this paper, the authors investigated the feasibility of using a single-phase non-contrast CT scan, a cheaper, simpler, and safer substituent, to detect resectable pancreatic mass and classify the detection as pancreatic ductal adenocarcinoma or other abnormalities (nonPDAC) or normal pancreas.
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S$^2$-MLP: Spatial-Shift MLP Architecture for Vision
TL;DR: S$2$-MLP as mentioned in this paper is a pure pure MLP architecture, which only contains channel-mixing MLP and utilizes a spatial-shift operation for communications between patches.
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.