computer-vision-7
Panoptic Segmentation
3260 papers • 126 benchmarks • 313 datasets
Panoptic Segmentation is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual instances of objects within those regions. In a given image, every pixel is assigned a semantic label, and pixels belonging to "things" classes (countable objects with instances, like cars and people) are assigned unique instance IDs. ( Image credit: Detectron2 )
(Image credit: Papersgraph)
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9 papers 28,397
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Ross B. Girshick, Piotr Dollár•Sun Mar 19 2017
This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
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