Abstract
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.
| Original language | English |
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| Title of host publication | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 5481-5490 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-6654-4509-2 |
| DOIs | |
| Publication status | Published - 13 Nov 2021 |
| Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Duration: 19 Jun 2021 → 25 Jun 2021 |
Conference
| Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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| Abbreviated title | CVPR |
| Period | 19/06/21 → 25/06/21 |