Image classification based on self-attention convolutional neural network

Abstract

Image classification technology is the most basic and important technical branch of computer vision. How to effectively extract effective information from images has become more and more urgent. First, we use the self-attention module to use the correlation between the features to weight and sum the features to get the image category. The self-attention mechanism is simpler to calculate, which greatly reduces the complexity of the model. Secondly, we have also made an optimization strategy for the complex CNN (Convolutional Neural Network) model. This article uses the global average pooling method to replace the fully connected method, which reduces the complexity of the model and generates fewer features. Finally, we verified the feasibility and effectiveness of our model on two data sets.