Abstract
Traditional content-based clothing classification has high requirements for image features. When there are many clothing styles, its accuracy is difficult to meet the application requirements of clothing classification. A parallel self-attention classification network based on deep learning method is proposed. The network adds a parallel self-attention compensation branch on the basis of ResNet50. This branch can improve the feature extraction quality in clothing classification tasks and gradually supplement the shallow detail information missing in the deep network. A comparative experiment was carried out on the DeepFashion dataset, and the experimental results proved the effectiveness of this method.
Published Version
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