Bridging the Catalog-to-Real Gap: Scalable Product Recognition via Multi-Stage Contrastive Learning

arXiv:2607.09888v1 Announce Type: new Abstract: Automated product recognition is a cornerstone of modern retail intelligence; however, accurately matching real-world, in-store images against extensive corporate catalogs remains a major scalability bottleneck for large-scale applications. In this work, we address this challenge by reformulating the task as an embedding-based cross-domain retrieval problem rather than a standard closed-set classification task. Specifically, we define the objective...