Method Details
Details for method 'Pixelwise Instance Segmentation with a Dynamically Instantiated Network'
Method overview
| name | Pixelwise Instance Segmentation with a Dynamically Instantiated Network |
| challenge | instance-level semantic labeling |
| details | We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label. Our method is based on an initial semantic segmentation module which feeds into an instance subnetwork. This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances. This part of our model is dynamically instantiated to produce a variable number of instances per image. Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals. As a result, it reasons about occlusions (unlike some related work, a single pixel cannot belong to multiple instances). |
| publication | Pixelwise Instance Segmentation with a Dynamically Instantiated Network Anurag Arnab and Philip Torr Computer Vision and Pattern Recognition (CVPR) 2017 http://www.robots.ox.ac.uk/~aarnab/instances_dynamic_network.html |
| project page / code | |
| used Cityscapes data | fine annotations, coarse annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | January, 2017 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 19.9906 |
| AP50% | 38.8278 |
| AP100m | 32.6009 |
| AP50m | 37.6238 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 16.4671 | 37.117 | 31.8394 | 32.8176 |
| rider | 16.6669 | 42.0743 | 28.2143 | 29.0221 |
| car | 25.6754 | 45.7449 | 41.1294 | 44.1784 |
| truck | 20.5676 | 30.2345 | 33.6878 | 39.8547 |
| bus | 29.9921 | 44.6628 | 48.9322 | 60.6816 |
| train | 23.3616 | 40.5202 | 35.3079 | 51.6885 |
| motorcycle | 17.1229 | 41.838 | 23.9906 | 24.9035 |
| bicycle | 10.0715 | 28.4305 | 17.7052 | 17.8442 |