Panoptic segmentation provides a comprehensive understanding of visual scenes by assigning each pixel a semantic class label and, for objects, an instance ID. While highly effective, traditional methods rely heavily on large-scale annotated datasets, posing significant challenges for scalability and adaptability. Additionally, the process of annotating images with panoptic labels is both labor-intensive and time-consuming, highlighting the importance of label-efficient approaches. In this talk, I will present an overview of various label-efficient methods, focusing on my recent work in unsupervised domain adaptation and semi-supervised learning. I will also discuss the emerging field of open-vocabulary panoptic segmentation, which extends recognition capabilities to categories beyond the training taxonomy.