Fine Grained Product

Fine-grained product classification focuses on accurately identifying specific product variations within broad categories, a crucial task for e-commerce and retail applications. Current research emphasizes developing robust zero-shot and few-shot learning methods, often leveraging vision-language models (VLMs) and incorporating textual information alongside visual data to improve classification accuracy, particularly for visually similar products. These advancements are driving improvements in product retrieval, personalized recommendations, and automated data extraction from unstructured sources, ultimately enhancing efficiency and user experience in online marketplaces.