Learning to Remember Beauty Products

This paper develops a deep learning model for the beauty product image retrieval problem. The proposed model has two main components-an encoder and a memory. The encoder extracts and aggregates features from a deep convolutional neural network at multiple scales to get feature embeddings. With the use of an attention mechanism and a data augmentation method, it learns to focus on foreground objects and neglect background on images, so can it extract more relevant features. The memory consists of representative states of all database images as its stacks, and it can be updated during training process. Based on the memory, we introduce a distance loss to regularize embedding vectors from the encoder to be more discriminative. Our model is fully end-to-end, requires no manual feature aggregation and post-processing. Experimental results on the Perfect-500K dataset demonstrate the effectiveness of the proposed model with a significant retrieval accuracy.