Coarse Label

Coarse labels represent a simplified, higher-level categorization of data, offering a practical alternative to detailed, fine-grained annotations which are often expensive and time-consuming to obtain. Current research focuses on leveraging coarse labels to improve various machine learning tasks, including image classification, semantic segmentation (particularly in LiDAR data for autonomous driving), and speech translation, often employing contrastive learning methods and large language models to bridge the gap between coarse and fine-grained information. This approach is particularly significant for addressing data scarcity issues in specialized domains, enabling more efficient training of models and potentially improving performance in applications where detailed labels are unavailable or impractical.