Method Details
Details for method 'Spatial Sampling Net'
Method overview
| name | Spatial Sampling Net |
| challenge | instance-level semantic labeling |
| details | We propose a network architecture to perform efficient scene understanding. This work presents three main novelties: the first is an Improved Guided Upsampling Module that can replace in toto the decoder part in common semantic segmentation networks. Our second contribution is the introduction of a new module based on spatial sampling to perform Instance Segmentation. It provides a very fast instance segmentation, needing only thresholding as post-processing step at inference time. Finally, we propose a novel efficient network design that includes the new modules and we test it against different datasets for outdoor scene understanding. |
| publication | Spatial Sampling Network for Fast Scene Understanding Davide Mazzini, Raimondo Schettini CVPR 2019 Workshop on Autonomous Driving http://openaccess.thecvf.com/content_CVPRW_2019/html/WAD/Mazzini_Spatial_Sampling_Network_for_Fast_Scene_Understanding_CVPRW_2019_paper.html |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | 0.00884 s Titan Xp |
| subsampling | 2 |
| submission date | March, 2019 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 9.1828 |
| AP50% | 16.8466 |
| AP100m | 16.4305 |
| AP50m | 21.3972 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 8.79089 | 22.0124 | 18.5868 | 19.8783 |
| rider | 3.19721 | 11.6965 | 5.83988 | 6.21282 |
| car | 24.0375 | 35.4222 | 41.001 | 49.3948 |
| truck | 9.95311 | 13.3939 | 18.1166 | 25.2318 |
| bus | 13.1632 | 19.2854 | 24.436 | 36.8475 |
| train | 8.48218 | 16.1681 | 14.3033 | 23.36 |
| motorcycle | 4.35087 | 11.3931 | 6.52919 | 7.50976 |
| bicycle | 1.48746 | 5.40145 | 2.63146 | 2.74299 |