Channel Attention Multi-Branch Network for Fine-Grained Image Recognition

The content of fine-grained image recognition research is the problem of sub-category recognition under broad categories. The key is to find the key regions in the image and extract effective features from them. Aiming at the problem that the existing methods cannot balance the accuracy and the amount of calculation when locating key areas, a multi-branch network that introduces an efficient channel attention module is proposed in this paper. First, the channel attention is introduced on the basis of the recurrent attention convolutional neural network to locate the target position in the image. Then, the traditional convolution operation is replaced with depthwise over parameterized convolution, which increases the parameters that the network can learn. Finally, the advanced attention part module is used to cut out multiple image key area components to capture rich local information. Experimental results show that the method has a better recognition effect in weakly supervised situations, and the recognition accuracy rates on the two commonly used fine-grained datasets Stanford Cars and Food-101 are 95.4% and 90.6 %, respectively.