Clothing Retrieval from Channel Attention to KN Loss Learning

Due to the high diversity of clothing types and names, using text to search may not find the desired clothes. Clothing retrieval via images can help users find their favorite clothes more conveniently. So, it is widely used in many online stores and platforms, and gradually replaces text search. Clothing retrieval performance is often affected due to factors such as shooting angle, occlusion, and lighting. Therefore, we propose a low-parameter clothing retrieval model without using any additional annotations or labels to aid learning. In the model, we use the classification capability of the Capsule Network with multiple residual convolution modules to learn features, and the VGG16 network is used as a clothing feature extractor to help the model learn features such as clothing color and texture. We add different attention mechanisms to the model for feature enhancement and design a new embedding method. We also adjust the loss function to sample multiple negative classes to improve the similarity clustering effect, and add weights to strengthen it. From the experimental results, one can find that our proposed method achieves better retrieval accuracy than several state-of-the-art models.