PRISM: Product Retrieval In Shopping Carts using Hybrid Matching
This addresses the practical problem of accurate product identification for retail applications, though it appears to be an incremental improvement combining existing techniques.
The paper tackles the challenging problem of product retrieval in retail settings where products have high visual similarity and query images may have different viewing angles. The proposed PRISM method achieves a 4.21% improvement in top-1 accuracy over state-of-the-art methods while maintaining real-time processing capabilities.
Compared to traditional image retrieval tasks, product retrieval in retail settings is even more challenging. Products of the same type from different brands may have highly similar visual appearances, and the query image may be taken from an angle that differs significantly from view angles of the stored catalog images. Foundational models, such as CLIP and SigLIP, often struggle to distinguish these subtle but important local differences. Pixel-wise matching methods, on the other hand, are computationally expensive and incur prohibitively high matching times. In this paper, we propose a new, hybrid method, called PRISM, for product retrieval in retail settings by leveraging the advantages of both vision-language model-based and pixel-wise matching approaches. To provide both efficiency/speed and finegrained retrieval accuracy, PRISM consists of three stages: 1) A vision-language model (SigLIP) is employed first to retrieve the top 35 most semantically similar products from a fixed gallery, thereby narrowing the search space significantly; 2) a segmentation model (YOLO-E) is applied to eliminate background clutter; 3) fine-grained pixel-level matching is performed using LightGlue across the filtered candidates. This framework enables more accurate discrimination between products with high inter-class similarity by focusing on subtle visual cues often missed by global models. Experiments performed on the ABV dataset show that our proposed PRISM outperforms the state-of-the-art image retrieval methods by 4.21% in top-1 accuracy while still remaining within the bounds of real-time processing for practical retail deployments.