Image classification model based on large kernel attention mechanism and relative position self-attention mechanism.

ABSTRACT: The Transformer has achieved great success in many computer vision tasks. With the in-depth exploration of it, researchers have found that Transformers can better obtain long-range features than convolutional neural networks (CNN). However, there will be a deterioration of local feature details when the Transformer extracts local features. Although CNN is adept at capturing the local feature details, it cannot easily obtain the global representation of features. In order to solve the above problems effectively, this paper proposes a hybrid model consisting of CNN and Transformer inspired by Visual Attention Net (VAN) and CoAtNet. This model optimizes its shortcomings in the difficulty of capturing the global representation of features by introducing Large Kernel Attention (LKA) in CNN whil

SUBMITTER: Liu S 

PROVIDER: S-EPMC10280586 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature