Method Details

Details for method 'Panoptic-DeepLab [Cityscapes-fine]'

name	Panoptic-DeepLab [Cityscapes-fine]
challenge	pixel-level semantic labeling
details	Our proposed bottom-up Panoptic-DeepLab is conceptually simple yet delivers state-of-the-art results. The Panoptic-DeepLab adopts dual-ASPP and dual-decoder modules, specific to semantic segmentation and instance segmentation respectively. The semantic segmentation prediction follows the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation prediction involves a simple instance center regression, where the model learns to predict instance centers as well as the offset from each pixel to its corresponding center. This submission exploits only Cityscapes fine annotations.
publication	Panoptic-DeepLab Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen https://arxiv.org/abs/1910.04751
project page / code
used Cityscapes data	fine annotations
used external data	ImageNet
runtime	n/a
subsampling	no
submission date	September, 2019
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