Supervised Panoptic Segmentation

Supervised panoptic segmentation aims to comprehensively understand images by simultaneously identifying and classifying objects (instance segmentation) and their semantic labels (semantic segmentation). Current research focuses on reducing reliance on expensive, fully annotated datasets by exploring weakly supervised approaches using point-level annotations or self-supervised learning with pseudo-labels generated from unlabeled or synthetic data. These methods, often employing fully convolutional networks or transformer architectures, improve segmentation accuracy and efficiency, impacting applications like autonomous driving and medical image analysis by enabling more robust and data-efficient model training.