Method Details
Details for method 'Semantic Instance Segmentation with a Discriminative Loss Function'
Method overview
| name | Semantic Instance Segmentation with a Discriminative Loss Function |
| challenge | instance-level semantic labeling |
| details | This method uses a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. The loss function encourages the network to map each pixel to a point in feature space so that pixels belonging to the same instance lie close together while different instances are separated by a wide margin. Previously listed as "PPLoss". |
| publication | Semantic Instance Segmentation with a Discriminative Loss Function Bert De Brabandere, Davy Neven, Luc Van Gool Deep Learning for Robotic Vision, workshop at CVPR 2017 https://arxiv.org/abs/1708.02551 |
| project page / code | https://github.com/DavyNeven/fastSceneUnderstanding |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | 2 |
| submission date | March, 2017 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 17.4741 |
| AP50% | 35.8662 |
| AP100m | 27.8474 |
| AP50m | 30.9631 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 13.4739 | 31.9628 | 25.0922 | 25.1403 |
| rider | 16.1671 | 40.7139 | 27.4539 | 28.1786 |
| car | 24.4367 | 43.1986 | 39.9743 | 44.0233 |
| truck | 16.7724 | 28.5141 | 24.3667 | 28.5748 |
| bus | 23.8738 | 39.1294 | 39.3879 | 47.7435 |
| train | 19.1593 | 35.6574 | 26.475 | 32.4971 |
| motorcycle | 15.2178 | 37.9132 | 22.1758 | 23.5113 |
| bicycle | 10.6918 | 29.8407 | 17.8536 | 18.0359 |