Beauty Product Retrieval Based on Regional Maximum Activation of Convolutions with Generalized Attention

Beauty and Personal care product retrieval has attracted more and more research attention for its value in real life. However, suffering from data variants and complex background, this task has been very challenging. In this paper, we propose a novel Generalized-attention Regional Maximal Activation of Convolutions (GRMAC) descriptor which helps to generate image features for retrieval. This method introduces attention mechanism to reduce the influence of clustered background and highlight the target, and thus contributes to enhancing the effectiveness of features and boosting the retrieval performance. Different from other attention-based methods, our method supports adjusting mask with a hyperparameter p, which is more flexible and accurate in real application. To demonstrate its effectiveness, we conduct experiments on the dataset containing more than half million personal care products (Perfect-500K) and obtain remarkable results. Furthermore, we try to fuse multiple features from different models for more improvements. And finally, our team (USTC_NELSLIP) ranked 1st in the Grand Challenge of AI Meets Beauty in ACM Multimedia 2019 with a MAP score of 0.408614. Our code is available at: https://github.com/gniknoil/Perfect500K-Beauty-and-Personal-Care-Products-Retrieval-Challenge