Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

11/22/2019 ∙ by Bowen Cheng, et al. ∙ 10

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, PanopticDeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. 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. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2 MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025 x 2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ ensemble of six models attains 42.7 2018 by a healthy margin of 1.5 on par with several topdown approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation.

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Code Repositories

panoptic-deeplab

This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)


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Poster_presentation_unlv_REUsummer2021

None


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Overview-of-REU-Summer-2021-Smart-Cities-UNLV-

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panoptic-deeplab

Implementation of the paper: https://arxiv.org/pdf/1911.10194.pdf


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PanopticCenter

PanopticCenter Network


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