Method Details
Details for method 'Panoptic-DeepLab [Cityscapes-fine]'
Method overview
| name | Panoptic-DeepLab [Cityscapes-fine] |
| challenge | instance-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. This entry fixes a minor inference bug (i.e., same trained model) for instance segmentation, compared to the previous submission. |
| 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 | October, 2019 |
| previous submissions | 1 |
Average results
| Metric | Value |
|---|---|
| AP | 34.6462 |
| AP50% | 57.2669 |
| AP100m | 50.4717 |
| AP50m | 53.133 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 34.3287 | 63.883 | 54.1593 | 54.2908 |
| rider | 28.8696 | 61.1347 | 43.8113 | 44.6366 |
| car | 55.0604 | 77.8674 | 76.3908 | 78.8617 |
| truck | 32.8259 | 42.0154 | 47.7383 | 52.7469 |
| bus | 41.4894 | 53.3573 | 63.3842 | 70.2216 |
| train | 36.6418 | 56.0755 | 49.2922 | 54.3213 |
| motorcycle | 26.3188 | 53.2601 | 35.3858 | 36.3494 |
| bicycle | 21.6352 | 50.5418 | 33.612 | 33.6361 |