Method Details

Details for method 'LevelSet R-CNN [COCO]'


Method overview

name LevelSet R-CNN [COCO]
challenge instance-level semantic labeling
details Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving. Currently, many state of the art models are based on the Mask R-CNN framework which, while very powerful, outputs masks at low resolutions which could result in imprecise boundaries. On the other hand, classic variational methods for segmentation impose desirable global and local data and geometry constraints on the masks by optimizing an energy functional. While mathematically elegant, their direct dependence on good initialization, non-robust image cues and manual setting of hyperparameters renders them unsuitable for modern applications. We propose LevelSet R-CNN, which combines the best of both worlds by obtaining powerful feature representations that are combined in an end-to-end manner with a variational segmentation framework. We demonstrate the effectiveness of our approach on COCO and Cityscapes datasets.
publication LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Namdar Homayounfar*, Yuwen Xiong*, Justin Liang*, Wei-Chiu Ma, Raquel Urtasun
ECCV 2020
project page / code
used Cityscapes data fine annotations
used external data ImageNet, COCO
runtime n/a
subsampling no
submission date March, 2020
previous submissions


Average results

Metric Value
AP 40.0128
AP50% 65.7059
AP100m 54.4868
AP50m 58.1338


Class results

Class AP AP50% AP100m AP50m
person 43.3661 76.2846 60.7964 61.0507
rider 33.8624 72.5066 47.7805 48.7313
car 58.9969 83.1619 77.6188 80.3708
truck 37.6435 49.3553 51.4051 60.1713
bus 49.3671 66.752 68.6348 76.5683
train 39.4466 58.7626 53.2084 60.9755
motorcycle 32.4831 61.7653 41.0268 41.8584
bicycle 24.9368 57.0586 35.4237 35.3439



Download results as .csv file

Benchmark page