Part-aware Panoptic Segmentation
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Panoptic Part Segmentation | Cityscapes Panoptic Parts (val) | PartPQ (All Parts)60.2 | 21 | |
| Panoptic Part Segmentation | PASCAL Panoptic Parts (PPP) (val) | PartPQ (All)38.3 | 21 | |
| Part Segmentation | PAS-P | mIoU PartS58.6 | 8 | |
| Panoptic Segmentation | CPP | PartPQ (All)61.4 | 6 |