Method Details
Details for method 'TASCNet-enhanced'
Method overview
| name | TASCNet-enhanced |
| challenge | panoptic semantic labeling |
| details | We proposed a joint network for panoptic segmentation, which is a variation of our previous work, TASCNet. (https://arxiv.org/pdf/1812.01192.pdf) A shared backbone (ResNeXt-101) pretrained on COCO detection is used. |
| publication | Learning to Fuse Things and Stuff Jie Li, Allan Raventos, Arjun Bhargava, Takaaki Tagawa, Adrien Gaidon Arxiv https://arxiv.org/abs/1812.01192 |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | COCO |
| runtime | n/a |
| subsampling | no |
| submission date | May, 2019 |
| previous submissions |
Average results
| Metric | All | Things | Stuff |
|---|---|---|---|
| PQ | 60.7117 | 53.4201 | 66.0146 |
| SQ | 81.0202 | 79.6543 | 82.0136 |
| RQ | 73.807 | 66.9877 | 78.7665 |
Class results
| Class | PQ | SQ | RQ |
|---|---|---|---|
| road | 98.1753 | 98.3049 | 99.8682 |
| sidewalk | 75.4642 | 84.1438 | 89.6848 |
| building | 87.8064 | 90.405 | 97.1257 |
| wall | 33.322 | 74.1578 | 44.9339 |
| fence | 35.2333 | 72.9537 | 48.2955 |
| pole | 53.9444 | 67.9571 | 79.3801 |
| traffic light | 52.5096 | 73.8356 | 71.117 |
| traffic sign | 66.771 | 77.6877 | 85.948 |
| vegetation | 90.014 | 91.3204 | 98.5695 |
| terrain | 43.0675 | 78.2308 | 55.0518 |
| sky | 89.8528 | 93.1526 | 96.4576 |
| person | 55.1516 | 77.3714 | 71.2817 |
| rider | 52.5931 | 74.1513 | 70.9268 |
| car | 66.8824 | 84.632 | 79.0273 |
| truck | 49.3571 | 85.3173 | 57.8512 |
| bus | 57.5856 | 86.5041 | 66.5698 |
| train | 55.6635 | 81.176 | 68.5714 |
| motorcycle | 46.9393 | 75.5429 | 62.1359 |
| bicycle | 43.1882 | 72.5396 | 59.5374 |