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Due to the complex attention mechanisms and model design, most existing vision Transformers (ViTs) can not perform as efficiently as convolutional neural networks (CNNs) in realistic industrial deployment scenarios, e.g.
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Mobilenetv2: Inverted residuals and linear bottlenecks
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Mingxing Tan and Quoc Le · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
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Pvtv2: Improved baselines with pyramid vision transformer
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
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Cvt: Introducing convolutions to vision transformers
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Co-scale conv-attentional image transformers
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
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Twins: Revisiting the design of spatial attention in vision transformers
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Cheng Cui, Ruoyu Guo, Yuning Du, Dongliang He, Fu Li, Zewu Wu, Qiwen Liu, Shilei Wen, Jizhou Huang, Xiaoguang Hu, et al · 2021
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Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan · 2021
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Cswin transformer: A general vision transformer backbone with cross-shaped windows
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Cmt: Convolutional neural networks meet vision transformers
Jianyuan Guo, Kai Han, Han Wu, Chang Xu, Yehui Tang, Chunjing Xu, and Yunhe Wang · 2021
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Rethinking spatial dimensions of vision transformers
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Uniformer: Unifying convolution and self-attention for visual recognition
Kunchang Li, Yali Wang, Junhao Zhang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao · 2022
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Sepvit: Separable vision transformer
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Efficientformer: Vision transformers at mobilenet speed
Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, and Jian Ren · 2022
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Separable self-attention for mobile vision transformers
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How do vision transformers work?
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Trt-vit: Tensorrt-oriented vision transformer
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Scalablevit: Rethinking the context-oriented generalization of vision transformer
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Metaformer is actually what you need for vision
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Resnest: Split-attention networks
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Understanding the robustness in vision transformers
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, and Jose M Alvarez · 2022
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