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Vision transformers have gained popularity recently, leading to the development of new vision backbones with improved features and consistent performance gains.
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2021
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2022
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X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in
2019
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2019
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R. Zhang, “Making convolutional networks shift-invariant again,” in
2019
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2020
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2020
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E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in
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2022
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2022
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W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, and S. Yan, “MetaFormer is actually what you need for vision,” in
2022
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X. Dong, J. Bao, D. Chen, W. Zhang, N. Yu, L. Yuan, D. Chen, and B. Guo, “Cswin transformer: A general vision transformer backbone with cross-shaped windows,” in
2022
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W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “PVT v2: Improved baselines with pyramid vision transformer,”
2022
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S. Wu, T. Wu, H. Tan, and G. Guo, “Pale transformer: A general vision transformer backbone with pale-shaped attention,” in
2022
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2022
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2022
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2023
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A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,”
2023
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Y. Xiong, Z. Li, Y. Chen, F. Wang, X. Zhu, J. Luo, W. Wang, T. Lu, H. Li, Y. Qiao
2024
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2024
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