ACANet: A Fine-grained Image Classification Optimization Method Based on Convolution and Attention Fusion

Abstract

The key to solve the problem of fine-grained image classification is to find the differentiation regions related to fine-grained features. In this paper, we try to add new network components to the original network and adjust various parameters to try to propose a new fine-grained image classification network. We propose a fine-grained image classification network based on the fusion of asymmetric convolution, convolution and self-attention mechanisms. Firstly, an enhanced module using asymmetric convolution to assist classical convolution proposed to help convolution learn deep features. Secondly, according to the common points of convolution and self-attention mechanism, we invented a fusion module of convolution and self-attention mechanism to improve the learning ability of the network.We integrate these two modules into the residual network and invent a new residual network .Finally, according to the experience, we design a new downsampling layer to adapt to the new component of the attention mechanism and improve the performance of the model. The experiment test on three publicly available datasets, and three methods for comparison. The results show that the new structure can effectively complete the task of fine-grained image classification, and the classification accuracy of different methods and different datasets are significantly improved.