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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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
TL;DR: In this article, the authors propose Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism, which conducts the dependencies discovery and representation aggregation at the sub-series level.
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RegionViT: Regional-to-Local Attention for Vision Transformers.
TL;DR: Zhang et al. as mentioned in this paper proposed a new architecture that adopts the pyramid structure and employ a novel regional-to-local attention rather than global self-attention in vision transformers, where each regional token is associated with a set of local tokens based on the spatial location.
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Learning Self-Similarity in Space and Time as Generalized Motion for Action Recognition
TL;DR: Li et al. as mentioned in this paper proposed a rich and robust motion representation based on spatio-temporal self-similarity (STSS), which represents each local region as similarities to its neighbors in space and time.
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FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
Chaoyang He,Alay Dilipbhai Shah,Zhenheng Tang,Di Fan,Adarshan Naiynar Sivashunmugam,Keerti Bhogaraju,Mita Shimpi,Li Shen,Xiaowen Chu,Mahdi Soltanolkotabi,A. Salman Avestimehr +10 more
TL;DR: In this paper, the authors proposed a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection.
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TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios
TL;DR: TPH-YOLOv5 as mentioned in this paper replaces the original prediction heads with Transformer Prediction Heads (TPH) to explore the prediction potential with self-attention mechanism and integrate convolutional block attention model (CBAM) to find attention region on scenarios with dense objects.
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.