基于关键点注意力和通道注意力的服装分类算法

In order to solve the problems of clothing landmark detection, category classification and attribute prediction, a novel deep neural network based on the combination of landmark attention mechanism and channel attention mechanism was proposed. First, the network predicts clothing landmarks by convoluting the input feature map to extract features, deconvoluting to restore the feature map size. Then, it acquires the connection between the landmarks by adding a non-local structure, thus, obtaining the landmark attention. The landmark attention module emphasizes the characteristics of the discriminative area in the clothing, and then new feature maps are generated. In addition, channel attention increases the weight of some feature maps which are more useful for category classification and attribute prediction. The experimental results on the DeepFashion dataset show that the proposed method can improve the accuracy of category classification and the recall rate of attribute prediction compared with the existing methods.