Textual Label

Textual labels, used to categorize data in machine learning, are undergoing significant advancements, focusing on improving their effectiveness in various applications, particularly in low-resource and open-vocabulary scenarios. Current research emphasizes developing methods to generate and enhance textual labels, often leveraging large language models and techniques like prompt engineering and contrastive learning to improve the representation of visual and other non-textual data. These improvements are crucial for enhancing the accuracy and interpretability of machine learning models across diverse fields, from image classification and information retrieval to multimodal emotion recognition and biomedical image analysis.