Enhanced Product Classification Using Learned Prompt Ensembling and Dual Interpolation with CLIP-Based Model

Retail product classification is a crucial technology due to its market size and potential. Several algorithms have been proposed; however, these approaches are not suitable due to their inability to accommodate new data without retraining or their insufficient performance caused by class names reflecting product-specific names. In this study, we adopt a CLIP-based model for retail product classification to overcome the challenges associated with model retraining for new products and the issue of unique class names. Our approach, learned prompt ensembling and dual interpolation (LPEDI), combines prompt learning and its ensembling with encoder fine-tuning, and employs dual interpolation for coefficient adjustment. The method outperforms existing solutions on two retail product datasets, achieving a 5.9% improvement for in-distribution data and a 4.5% gain for out-of-distribution data. These results establish LPEDI as a practical and effective solution for retail product classification.