  The paper proposes an efficient structure for enhancing the performance of
mobile-friendly vision transformer with small computational overhead. The
vision transformer (ViT) is very attractive in that it reaches outperforming
results in image classification, compared to conventional convolutional neural
networks (CNNs). Due to its need of high computational resources,
MobileNet-based ViT models such as MobileViT-S have been developed. However,
their performance cannot reach the original ViT model. The proposed structure
relieves the above weakness by storing the information from early attention
stages and reusing it in the final classifier. This paper is motivated by the
idea that the data itself from early attention stages can have important
meaning for the final classification. In order to reuse the early information
in attention stages, the average pooling results of various scaled features
from early attention stages are used to expand channels in the fully-connected
layer of the final classifier. It is expected that the inductive bias
introduced by the averaged features can enhance the final performance. Because
the proposed structure only needs the average pooling of features from the
attention stages and channel expansions in the final classifier, its
computational and storage overheads are very small, keeping the benefits of
low-cost MobileNet-based ViT (MobileViT). Compared with the original MobileViTs
on the ImageNet dataset, the proposed ExMobileViT has noticeable accuracy
enhancements, having only about 5% additional parameters.
