Transcription of Panoptic Segmentation
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Panoptic Segmentation
Panoptic Segmentation Alexander Kirillov1,2 Kaiming He1 Ross Girshick1 Carsten Rother2 Piotr Dolla r1. 1 2. Facebook AI Research (FAIR) HCI/IWR, Heidelberg University, Germany Abstract We propose and study a task we name Panoptic segmen- tation (PS). Panoptic Segmentation unifies the typically dis- tinct tasks of semantic Segmentation (assign a class label to each pixel) and instance Segmentation (detect and seg- ment each object instance). The proposed task requires (a) image (b) semantic Segmentation generating a coherent scene Segmentation that is rich and complete, an important step toward real-world vision sys- tems. While early work in computer vision addressed re- lated image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or as- sociated recognition challenges.
(in essence, asophisticated form of non-maximum suppres-sion). Our heuristic establishes a baseline for PS and gives us insights into the main algorithmic challenges it presents. We study both human and machine performance on three popular segmentation datasets that have both stuff and things annotations. This includes the Cityscapes [6],
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