  Self-attention-based vision transformers (ViTs) have emerged as a highly
competitive architecture in computer vision. Unlike convolutional neural
networks (CNNs), ViTs are capable of global information sharing. With the
development of various structures of ViTs, ViTs are increasingly advantageous
for many vision tasks. However, the quadratic complexity of self-attention
renders ViTs computationally intensive, and their lack of inductive biases of
locality and translation equivariance demands larger model sizes compared to
CNNs to effectively learn visual features. In this paper, we propose a
light-weight and efficient vision transformer model called DualToken-ViT that
leverages the advantages of CNNs and ViTs. DualToken-ViT effectively fuses the
token with local information obtained by convolution-based structure and the
token with global information obtained by self-attention-based structure to
achieve an efficient attention structure. In addition, we use position-aware
global tokens throughout all stages to enrich the global information, which
further strengthening the effect of DualToken-ViT. Position-aware global tokens
also contain the position information of the image, which makes our model
better for vision tasks. We conducted extensive experiments on image
classification, object detection and semantic segmentation tasks to demonstrate
the effectiveness of DualToken-ViT. On the ImageNet-1K dataset, our models of
different scales achieve accuracies of 75.4% and 79.4% with only 0.5G and 1.0G
FLOPs, respectively, and our model with 1.0G FLOPs outperforms LightViT-T using
global tokens by 0.7%.
