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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Large-scale ground truth data sets are of crucial importance for deep learning based segmentation models, but annotating per-pixel masks is prohibitively time consuming.
The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.
We present a novel dataset for training and benchmarking semantic SLAM methods.
Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis.
SOTA for Nuclear Segmentation on Cell17
This paper aims to reduce the time to annotate images for the panoptic segmentation task, which requires annotating segmentation masks and class labels for all object instances and stuff regions.
A common approach involves the fusion of instance segmentation (for "things") and semantic segmentation (for "stuff") proposals into a non-overlapping placement of segments, and resolves occlusions (or overlaps) between segments based on confidence scores.
This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level.