Semantic Label

Semantic labeling involves assigning meaningful labels to data, such as pixels in images or segments of text, to facilitate computer understanding and analysis. Current research focuses on improving the accuracy and efficiency of semantic labeling, particularly in open-vocabulary settings and with limited labeled data, employing techniques like contrastive learning, transformer-based models, and leveraging foundation models like SAM for pseudo-label generation. This field is crucial for advancing various applications, including image segmentation in robotics and autonomous driving, multi-label image recognition, and enhancing the explainability and robustness of large language models.