Part-aware Panoptic Segmentation
This work defines a novel task for computer vision researchers interested in multi-level scene analysis, though it is incremental as it combines existing segmentation methods.
The paper introduces Part-aware Panoptic Segmentation (PPS), a new scene understanding task that unifies scene parsing and part parsing, and provides annotations on Cityscapes and Pascal VOC datasets along with a Part-aware Panoptic Quality (PartPQ) metric for evaluation.
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single task.