Part-aware Panoptic Segmentation

被引:35
作者
de Geus, Daan [1 ]
Meletis, Panagiotis [1 ]
Lu, Chenyang [1 ]
Wen, Xiaoxiao [2 ]
Dubbelman, Gijs [1 ]
机构
[1] Eindhoven Univ Technol, Eindhoven, Netherlands
[2] Univ Amsterdam, Amsterdam, Netherlands
来源
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 | 2021年
关键词
HUMAN POSE ESTIMATION;
D O I
10.1109/CVPR46437.2021.00544
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:5481 / 5490
页数:10
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