A Novel Clothing Attribute Representation Network-Based Self-Attention Mechanism

As highly increasing of on-line fashions retail industry, automatic recognition and representation of clothing items have huge potentials. With the help of deep learning methods, many clothing attribute representation models have been proposed. However, these models are mainly suitable for coarse-grained classification which are not suitable for clothing attribute representation. To address such a problem, in this article, we propose a novel network structure named SAC, which is a combination of CNNs and Self-attention mechanism and can represent clothing attributes more fine-grained. Besides, we use Grad-CAM to visualize which part of the clothing attributes is more concerned by customers. Finally, a new labeled clothing dataset is introduced in this article, which is expected to be helpful to the researchers who are working in fashion domains for image representation.