Panoptic Segmentation

Panoptic segmentation aims to comprehensively understand a scene by simultaneously segmenting and classifying all objects and regions, including both "things" (individual objects) and "stuff" (amorphous regions). Current research focuses on improving accuracy and efficiency, particularly in challenging scenarios like occlusion, dynamic environments, and open-vocabulary settings, often employing transformer-based architectures, mask-based methods, and diffusion models. This task is crucial for various applications, including autonomous driving, robotics, and medical image analysis, driving advancements in both model design and benchmark datasets.