Sequencer: Deep LSTM for Image Classification

@article{Tatsunami2022SequencerDL,
  title={Sequencer: Deep LSTM for Image Classification},
  author={Yuki Tatsunami and Masato Taki},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.01972}
}
In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance… 

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