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In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms.
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MMDetection: Open mmlab detection toolbox and benchmark
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Local relation networks for image recognition
Han Hu, Zheng Zhang, Zhenda Xie, and Stephen Lin · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Stand-alone self-attention in vision models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jonathon Shlens · 2019
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Lambdanetworks: Modeling long-range interactions without attention
Irwan Bello · 2021
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Rethinking attention with performers
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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, and Neil Houlsby · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
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Deformable detr: Deformable transformers for end-to-end object detection
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