Learning to Fuse Things and Stuff
We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain these two output distributions through a global things and stuff binary mask to enforce cross-task consistency. Our proposed unified network is competitive with the state of the art on several benchmarks for panoptic segmentation as well as on the individual semantic and instance segmentation tasks.
PDF Abstract
Datasets
MS COCO
Cityscapes
Mapillary Vistas Dataset
Results from the Paper
Ranked #26 on
Panoptic Segmentation
on Cityscapes val
(using extra training data)
