  With the achievements of Transformer in the field of natural language
processing, the encoder-decoder and the attention mechanism in Transformer have
been applied to computer vision. Recently, in multiple tasks of computer vision
(image classification, object detection, semantic segmentation, etc.),
state-of-the-art convolutional neural networks have introduced some concepts of
Transformer. This proves that Transformer has a good prospect in the field of
image recognition. After Vision Transformer was proposed, more and more works
began to use self-attention to completely replace the convolutional layer. This
work is based on Vision Transformer, combined with the pyramid architecture,
using Split-transform-merge to propose the group encoder and name the network
architecture Aggregated Pyramid Vision Transformer (APVT). We perform image
classification tasks on the CIFAR-10 dataset and object detection tasks on the
COCO 2017 dataset. Compared with other network architectures that use
Transformer as the backbone, APVT has excellent results while reducing the
computational cost. We hope this improved strategy can provide a reference for
future Transformer research in computer vision.
