Matting Enhanced Mask R-CNN

We propose a novel and effective method for high-quality instance segmentation. Top-performing "detect-then-segment" approaches (e.g., Mask R-CNN) rely on region-of-interest (ROI) cropping operations to obtain the final masks, but their performance is restricted by blurry boundary and aver...

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Bibliographic Details
Main Authors: Ma, Lufan, Dong, Bin, Yan, Jiangpeng, Li, Xiu
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Estimation
Head
image matting
Image segmentation
Instance segmentation
Schedules
Training
Uncertainty
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Summary:We propose a novel and effective method for high-quality instance segmentation. Top-performing "detect-then-segment" approaches (e.g., Mask R-CNN) rely on region-of-interest (ROI) cropping operations to obtain the final masks, but their performance is restricted by blurry boundary and average loss weight. Here, we develop a unique perspective of image segmentation as an image matting problem. Our method, termed MMask R-CNN, enjoys two advantages: 1) Present a novel matting enhanced mask head to generate trimap-based mat-ting features as auxiliary priors, which enhance accurate estimation of alpha values for boundary pixels. 2) Explicitly represents the classification uncertainty with confidence indicator alpha matte, and design a Uncertainty-Aware Binary Cross-Entropy Loss to assign larger weights to pixels with higher uncertainty. We evaluate the proposed method through extensive experiments on the COCO dataset. The experimental results show that our method outperforms well-tuned Mask R-CNN baseline by 2.3% AP.
ISSN:1945-788X
DOI:10.1109/ICME51207.2021.9428183