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Transformers have recently shown superior performances on various vision tasks.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
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Imagenet: A large-scale hierarchical image database
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Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep residual learning for image recognition
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Deep networks with stochastic depth
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Deformable convolutional networks
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Mask r-cnn
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Feature pyramid networks for object detection
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Focal loss for dense object detection
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Decoupled weight decay regularization
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Attention is all you need
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mixup: Beyond empirical risk minimization
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Gcnet: Non-local networks meet squeeze-excitation networks and beyond
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Convolutional networks with dense connectivity
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Panoptic feature pyramid networks
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Cutmix: Regularization strategy to train strong classifiers with localizable features
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Randaugment: Practical automated data augmentation with a reduced search space
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An image is worth 16x16 words: Transformers for image recognition at scale
Swin transformer: Hierarchical vision transformer using shifted windows
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On the integration of self-attention and convolution, 2021
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3d object detection with pointformer
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Bottleneck transformers for visual recognition
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Cvt: Introducing convolutions to vision transformers
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Vision transformer with progressive sampling
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Multi-scale vision longformer: A new vision transformer for high-resolution image encoding
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