Few-shot Learning Based on Diversity Scores for Improving the Recognition of Unknown Items in Logistics Warehouses

Abstract

In logistics warehouses, deep learning is used to automate picking operations, addressing the growing e-commerce market and declining labor force. Enhancing picking capabilities requires high-performance recognition of various items. However, deep learning models often degrade in performance when recognizing unknown items, necessitating additional learning with extensive training data. Few-shot learning, which reduces the amount of training data, struggles with recognizing parts of complex-shaped items and has low performance when objects are partially occluded. This paper proposes a few-shot learning framework to solve these issues. By calculating a diversity score for each unknown image and determining the appropriate number of images per class, it becomes possible to learn from low-performance images. Combining data augmentation