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
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Can Vision Transformers Learn without Natural Images
TL;DR: In this paper, a pre-trained Vision Transformers (ViT) model without any image collections and annotation labor is proposed, which partially outperforms self-supervised learning (SSL) methods like SimCLRv2 and MoCov2.
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TubeR: Tube-Transformer for Action Detection.
TL;DR: In this paper, the authors propose a transformer-based network for end-to-end action detection, with an encoder and decoder optimized for modeling action tubes with variable lengths and aspect ratios.
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Object-Region Video Transformers.
Roei Herzig,Elad Ben-Avraham,Karttikeya Mangalam,Amir Bar,Gal Chechik,Anna Rohrbach,Trevor Darrell,Amir Globerson +7 more
TL;DR: In this paper, an object-region video transformer (ORViT) is proposed to fuse object-centric representations throughout multiple transformer layers, including appearance and dynamics, which can capture trajectory interactions.
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Beyond Mono to Binaural: Generating Binaural Audio from Mono Audio with Depth and Cross Modal Attention
TL;DR: In this paper, a hierarchical attention mechanism was proposed to encode image, depth and audio features jointly for audio binauralization, which outperformed state-of-the-art methods.
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Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John J. Miller,Rohan Taori,Aditi Raghunathan,Shiori Sagawa,Pang Wei Koh,Vaishaal Shankar,Percy Liang,Yair Carmon,Ludwig Schmidt +8 more
TL;DR: In this article, the authors empirically show that out-of-distribution performance is strongly correlated with the performance of a wide range of models and distribution shifts and provide a candidate theory based on a Gaussian data model that shows how changes in the data covariance arising from distribution shift can affect the observed correlations.
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.