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Recently, vision transformers (ViTs) have superseded convolutional neural networks in numerous applications, including classification, detection, and segmentation.
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2019
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R. Li, Y. Wang, F. Liang, H. Qin, J. Yan, and R. Fan, “Fully quantized network for object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2810–2819
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2021
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2021
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2023
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Y. Liu, P. Huang, F. Yang, K. Huang, and L. Shu, “Quasyncfl: Asynchronous federated learning with quantization for cloud-edge-terminal collaboration enabled aiot,” IEEE Internet of Things Journal , 2023
2023
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Z. Li, J. Xiao, L. Yang, and Q. Gu, “Repq-vit: Scale reparameterization for post-training quantization of vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 227–17 236
2023
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Y. Li, J. Hu, Y. Wen, G. Evangelidis, K. Salahi, Y. Wang, S. Tulyakov, and J. Ren, “Rethinking vision transformers for mobilenet size and speed,” in Proceedings of the IEEE international conference on computer vision , 2023
2023
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Y. Liu, H. Yang, Z. Dong, K. Keutzer, L. Du, and S. Zhang, “Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 20 321–20 330
2023
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D. Yang and Z. Luo, “A parallel processing cnn accelerator on embedded devices based on optimized mobilenet,” IEEE Internet of Things Journal , 2023
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2023
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