Quantity Aware

"Quantity-aware" research focuses on effectively leveraging numerical information within various data types, aiming to improve model performance and address limitations arising from data scarcity or noise. Current research explores this theme across diverse applications, including machine translation, image annotation, and information retrieval, often employing techniques like contrastive learning, deep neural networks (e.g., 3D-Unet, CNNs), and novel data selection methods to optimize the balance between data quantity and quality. These advancements have significant implications for improving the accuracy and efficiency of machine learning models in numerous fields, from natural language processing and computer vision to robotics and economic modeling.