Single Label Annotation

Single-label annotation, where each data point receives only one label despite potentially possessing multiple, presents a challenge in machine learning, particularly for tasks like multi-label classification and temporal grounding. Research focuses on mitigating this limitation through techniques like self-training, multi-task learning, and novel architectures that leverage relationships between annotators or incorporate consistency losses to improve predictions. These approaches aim to improve model performance and reduce the reliance on expensive, exhaustive annotation, impacting various fields including object detection, natural language processing, and video analysis.