Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance).
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The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects and distinct regions in the image and an evaluator net that chooses the best segments to be merged into the segmentation map.
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
#2 best model for Panoptic Segmentation on Cityscapes val (using extra training data)
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.
#12 best model for Panoptic Segmentation on Cityscapes val
We propose and study a task we name panoptic segmentation (PS).
#3 best model for Panoptic Segmentation on Cityscapes val (using extra training data)