Method Details

Details for method 'PolyTransform + SegFix + BPR'


Method overview

name PolyTransform + SegFix + BPR
challenge instance-level semantic labeling
details Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix baseline, we reached 1st place on the Cityscapes leaderboard.
publication Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Chufeng Tang*, Hang Chen*, Xiao Li, Jianmin Li, Zhaoxiang Zhang, Xiaolin Hu
CVPR 2021
project page / code
used Cityscapes data fine annotations
used external data ImageNet, COCO
runtime n/a
subsampling no
submission date November, 2020
previous submissions


Average results

Metric Value
AP 42.6512
AP50% 66.5055
AP100m 57.4983
AP50m 60.7325


Class results

Class AP AP50% AP100m AP50m
person 46.0349 76.9908 63.3646 63.4898
rider 37.1066 72.3817 51.5779 52.2112
car 62.7507 83.7732 81.1229 83.6874
truck 41.2719 52.6667 55.8305 64.2081
bus 52.6533 68.5697 70.9607 77.7261
train 43.7216 63.3475 59.933 66.5914
motorcycle 32.6051 59.413 41.9452 42.6975
bicycle 25.0652 54.9011 35.2521 35.2482



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