[2T1-GS-10] AI application

[2T1-GS-10-04] Product image identification by means of one-shot deep metric learning

〇Kanya Ishizaka¹, Aoi Kamo¹ (1. FUJIFILM Business Innovation Corp.)

[[Online]]

Keywords:deep metric learning, image recognition, retail

In the retail industry, there is a growing need for AI to identify products on product shelves in order to improve the efficiency of product management and planogram. Image identification technology, which identifies individual product images detected by object detection from product shelves by matching them with database images, is required to distinguish hundreds to thousands of products, including those with similar designs, under different lighting environments and display conditions for each store. In addition, it is required to be able to follow frequent product design changes. Furthermore, the number of images that can be used for learning is usually limited for each product. Deep metric learning (DML) is an effective approach to such problems. In this study, we propose data augmentation from one-shot images, clusterwise attractive/repulsive loss, epoch-by-epoch pairwise semi-hard negative mining, utilization of self-attention mechanism in backbone CNN, etc., and tried to acquire performance. Recall@5 performance achieved about 97% for the trained category and about 95% for the untrained category. Due to the characteristics of DML, it is possible to improve the efficiency of correcting mismatching.