Improved deep learning image classification algorithm based on Swin Transformer V2.

ABSTRACT: While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extractio

SUBMITTER: Wei J 

PROVIDER: S-EPMC10703013 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature