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).
( Image credit: Detectron2 )
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Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
#5 best model for Instance Segmentation on COCO test-dev
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
#3 best model for Panoptic Segmentation on Cityscapes val (using extra training data)
We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
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.
#13 best model for Panoptic Segmentation on Cityscapes val
In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances.
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.