Granularity Annotation

Granularity annotation focuses on creating datasets with annotations at varying levels of detail, from broad categories to fine-grained specifics, improving the accuracy and applicability of machine learning models. Current research emphasizes developing methods for automatically generating these multi-granular annotations, particularly for images and videos, often leveraging pre-trained models and incorporating interactive elements to refine annotations. This work is crucial for advancing various applications, including medical image analysis, content moderation, and scene understanding, by enabling the training of more robust and nuanced AI systems. The development of efficient annotation techniques and large-scale datasets with multi-granular labels is driving significant progress in these fields.