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A state-of-the-art bottom-up method for panoptic segmentation

A state-of-the-art bottom-up method for panoptic segmentation

Panoptic-DeepLab (CVPR 2020)

Panoptic-DeepLab is a state-of-the-art bottom-up method for panoptic segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image as well as instance labels (e.g. an id of 1, 2, 3, etc) to pixels belonging to thing classes.

What's New

  • We release a detailed technical report with implementation details
    and supplementary analysis on Panoptic-DeepLab. In particular, we find center prediction is almost perfect and the bottleneck of
    bottom-up method still lies in semantic segmentation
  • It is powered by the PyTorch deep learning framework.
  • Can be trained even on 4 1080TI GPUs (no need for 32 TPUs!).

Citing Panoptic-DeepLab

If you find this code helpful in your research or wish to refer to the baseline results, please use the following BibTeX entry.

@inproceedings{cheng2020panoptic,
  title={Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation},
  author={Cheng, Bowen and Collins, Maxwell D and Zhu, Yukun and Liu, Ting and Huang, Thomas S and Adam, Hartwig and Chen, Liang-Chieh},
  booktitle={CVPR},
  year={2020}
}

@inproceedings{cheng2019panoptic,
  title={Panoptic-DeepLab},
  author={Cheng, Bowen and Collins, Maxwell D and Zhu, Yukun and Liu, Ting and Huang, Thomas S and Adam, Hartwig and Chen, Liang-Chieh},
  booktitle={ICCV COCO + Mapillary Joint Recognition Challenge Workshop},
  year={2019}
}

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