  Panoptic segmentation is an important computer vision task which combines
semantic and instance segmentation. It plays a crucial role in domains of
medical image analysis, self-driving vehicles, and robotics by providing a
comprehensive understanding of visual environments. Traditionally, deep
learning panoptic segmentation models have relied on dense and accurately
annotated training data, which is expensive and time consuming to obtain.
Recent advancements in self-supervised learning approaches have shown great
potential in leveraging synthetic and unlabelled data to generate pseudo-labels
using self-training to improve the performance of instance and semantic
segmentation models. The three available methods for self-supervised panoptic
segmentation use proposal-based transformer architectures which are
computationally expensive, complicated and engineered for specific tasks. The
aim of this work is to develop a framework to perform embedding-based
self-supervised panoptic segmentation using self-training in a
synthetic-to-real domain adaptation problem setting.
