[3M1-GS-10] AI application:

[3M1-GS-10-04] Few-shot Learning Based on Diversity Scores for Improving the Recognition of Unknown Items in Logistics Warehouses

〇Kazuma Komoda¹, Ping Jiang ¹, Haifeng Han¹, Junichiro Ooga¹ (1. Toshiba Corporation)

Keywords:Few shot learning, Object Handling, Instance Segmentation

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