  In this paper, we propose a method using the fusion of CNN and transformer
structure to improve image classification performance. In the case of CNN,
information about a local area on an image can be extracted well, but there is
a limit to the extraction of global information. On the other hand, the
transformer has an advantage in relatively global extraction, but has a
disadvantage in that it requires a lot of memory for local feature value
extraction. In the case of an image, it is converted into a feature map through
CNN, and each feature map's pixel is considered a token. At the same time, the
image is divided into patch areas and then fused with the transformer method
that views them as tokens. For the fusion of tokens with two different
characteristics, we propose three methods: (1) late token fusion with parallel
structure, (2) early token fusion, (3) token fusion in a layer by layer. In an
experiment using ImageNet 1k, the proposed method shows the best classification
performance.
