Method Details
Details for method 'Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas]'
Method overview
| name | Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas] |
| challenge | panoptic semantic labeling |
| details | We revisit the architecture design of Wide Residual Networks. We design a baseline model by incorporating the simple and effective Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets. Its network capacity is further scaled up or down by adjusting the width (i.e., channel size) and depth (i.e., number of layers), resulting in a family of SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such a simple scaling scheme, coupled with grid search, identifies several SWideRNets that significantly advance state-of-the-art performance on panoptic segmentation datasets in both the fast model regime and strong model regime. |
| publication | Scaling Wide Residual Networks for Panoptic Segmentation Liang-Chieh Chen, Huiyu Wang, Siyuan Qiao https://arxiv.org/abs/2011.11675 |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | ImageNet, Mapillary Vistas Research Edition |
| runtime | n/a |
| subsampling | no |
| submission date | November, 2020 |
| previous submissions |
Average results
| Metric | All | Things | Stuff |
|---|---|---|---|
| PQ | 67.8211 | 60.9113 | 72.8463 |
| SQ | 83.7937 | 81.7757 | 85.2613 |
| RQ | 80.214 | 74.4178 | 84.4295 |
Class results
| Class | PQ | SQ | RQ |
|---|---|---|---|
| road | 98.6622 | 98.7272 | 99.9341 |
| sidewalk | 79.9432 | 86.6463 | 92.2638 |
| building | 90.2671 | 92.2119 | 97.8909 |
| wall | 46.4352 | 78.5224 | 59.1362 |
| fence | 48.7728 | 77.7505 | 62.7299 |
| pole | 69.2895 | 74.8507 | 92.5703 |
| traffic light | 60.6986 | 80.3888 | 75.5062 |
| traffic sign | 76.0908 | 83.2338 | 91.4182 |
| vegetation | 91.7482 | 92.5039 | 99.1831 |
| terrain | 47.6531 | 79.7153 | 59.7791 |
| sky | 91.7492 | 93.3241 | 98.3125 |
| person | 59.7505 | 78.3998 | 76.2125 |
| rider | 58.2818 | 75.5672 | 77.1257 |
| car | 71.9981 | 85.5011 | 84.2072 |
| truck | 59.4246 | 88.3023 | 67.2968 |
| bus | 69.0872 | 89.345 | 77.3263 |
| train | 62.8695 | 85.685 | 73.3728 |
| motorcycle | 55.1678 | 77.4937 | 71.19 |
| bicycle | 50.7112 | 73.911 | 68.6111 |