Weakly-supervised panoptic segmentation

Weakly-supervised panoptic segmentation is an image analysis technique in the field of computer vision that aims to achieve precise classification and instance recognition of every pixel in an image using limited annotated data. This method combines the strengths of semantic segmentation and instance segmentation, enhancing the model's generalization and robustness while reducing the cost of manual annotation. The application value of weakly-supervised panoptic segmentation lies in its ability to effectively address the challenge of annotating large-scale datasets, making it suitable for scenarios such as autonomous driving, medical image analysis, and remote sensing image processing.