  Motivated by the success of Transformers in natural language processing (NLP)
tasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to
the vision domain. However, pure Transformer architectures often require a
large amount of training data or extra supervision to obtain comparable
performance with convolutional neural networks (CNNs). To overcome these
limitations, we analyze the potential drawbacks when directly borrowing
Transformer architectures from NLP. Then we propose a new
\textbf{Convolution-enhanced image Transformer (CeiT)} which combines the
advantages of CNNs in extracting low-level features, strengthening locality,
and the advantages of Transformers in establishing long-range dependencies.
Three modifications are made to the original Transformer: \textbf{1)} instead
of the straightforward tokenization from raw input images, we design an
\textbf{Image-to-Tokens (I2T)} module that extracts patches from generated
low-level features; \textbf{2)} the feed-froward network in each encoder block
is replaced with a \textbf{Locally-enhanced Feed-Forward (LeFF)} layer that
promotes the correlation among neighboring tokens in the spatial dimension;
\textbf{3)} a \textbf{Layer-wise Class token Attention (LCA)} is attached at
the top of the Transformer that utilizes the multi-level representations.
  Experimental results on ImageNet and seven downstream tasks show the
effectiveness and generalization ability of CeiT compared with previous
Transformers and state-of-the-art CNNs, without requiring a large amount of
training data and extra CNN teachers. Besides, CeiT models also demonstrate
better convergence with $3\times$ fewer training iterations, which can reduce
the training cost significantly\footnote{Code and models will be released upon
acceptance.}.
