AI Technology

New SOTA on Instance Segmentation: Mask Scoring R-CNN Tops Mask R-CNN on COCO

Mask R-CNN (Regional Convolutional Neural Network) has been the state-of-the-art model for object instance segmentation since it was proposed by Facebook Research Scientist Kaiming He in 2017 and won Best Paper at ICCV the same year.

Mask R-CNN (Regional Convolutional Neural Network) has been the state-of-the-art model for object instance segmentation since it was proposed by Facebook Research Scientist Kaiming He in 2017 and won Best Paper at ICCV the same year. Mask R-CNN utilizes a relatively simple method to achieve its success in tasks of object detection, instance segmentation, and keypoint detection.

Mask Scoring R-CNN (MS R-CNN) is a new model proposed by a team from HUST (Huazhong University of Science & Technology) and Horizon Robotics Inc. which tweaks a Mask R-CNN based algorithm to optimize the scoring of instance segmentation masks. The paper Mask Scoring R-CNN has been accepted by CVPR 2019 and demonstrates new SOTA results, consistently outperforming Mask R-CNN on the COCO benchmark for instance segmentation.

“Scoring” is the core of the proposed method: The authors find that previous methods including Mask R-CNN treat the confidence of instance classification the same as the mask quality (measured with IoU, Intersection-over-Union) although they are usually not well correlated.

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The new method uses a network to learn the quality of the predicted instance masks via regression (measured with a MaskIoU score) and then penalize the instance mask score if the classification score is high while the actual mask quality is low.

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The researchers conducted evaluation experiments on the COCO dataset, and AP (average precision over IoU thresholds) at different scales (AP@0.5, AP@0.75, APs, APm, APl) were used as evaluation metrics.

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The results show that no matter what backbone network is used, MS R-CNN can always outperform Mask R-CNN by more than one percent.

The Mask Scoring R-CNN research paper is on arXiv and the code has been open-sourced on Github.


Author: Mos Zhang | Editor: Michael Sarazen

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