Attention-driven Unsupervised Image Retrieval for Beauty Products with Visual and Textual Clues

Abstract

Beauty and personal care product retrieval (BPCR) aims to match a query image of an item to examples of the same item in a large database. The task is extremely challenging because a small number of ground-truth examples have to be found in a large search space. Previous works mostly search only with visual representations and have not made full use of the product descriptions. Since many noisy examples only have subtle visual differences comparing to the ground-truth examples (e.g. similar packaging but different brands) and those differences (e.g. product brands) are especially hard to be captured only by visual features, methods merely based on visual feature similarities can easily regard those noisy examples as examples of the same item in the query image. We notice that the product descriptions are good sources for capturing those subtle visual differences. Therefore, we propose a search method utilizing both images and product descriptions in this work. Before searching, we not only prepare attention-based visual features for each database image but also a textual index (TI) that matches each database example to other examples with similar product descriptions. During searching, the visual feature of the query image is firstly searched in the whole database and then searched in a subset obtained by looking up the TI. Finally, the second result is used to refine the initial result. Since the subset examples usually have similar properties (e.g. brands and type), the noisy examples in the initial result can be effectively replaced. We have experimentally proved the effectiveness of the proposed method on the validation set of the Perfect-500K dataset. Our team (NTU-Beauty) achieved the 3rd place in the leader board of the Grand Challenge of AI Meets Beauty in ACM Multimedia 2020. Our code is available at: https://github.com/jingwenh/2020-ai-meets-beauty_ntubeauty.git.