Grocery Recognition in the Wild: A New Mining Strategy for Metric Learning

Recognizing grocery products at scale is an open issue for computer-vision systems due to their subtle visual differences. Typically the problem is addressed as a classification problem, e.g., by learning a CNN, for which all classes that are to be distinguished need to be known at training time. We instead observe that the products within stores change over time. Sometimes new products are put on shelves, or existing appearances of products are changed. In this work, we demonstrate the use of deep metric learning for grocery recognition, whereby classes during inference are unknown while training. We also propose a new triplet mining strategy that uses all known classes during training while preserving the ability to perform cross-folded validation. We demonstrate the applicability of the proposed mining strategy using different, publicly available real-world grocery datasets. The proposed approach preserves the ability to distinguish previously unseen groceries while increasing the precision by up to 5 percent.