Paper reading: Mask RCNN-优快云博客

本文链接：https://blog.youkuaiyun.com/u011107912/article/details/78980107

Mask R-CNN是一种用于对象实例分割的概念简单、灵活且通用的框架，它在检测图像中物体的同时生成每个实例的高质量分割掩模。该方法通过在Faster R-CNN的基础上增加一个预测对象掩模的分支，与现有的边界框识别分支并行工作。此外，Mask R-CNN在COCO挑战赛的所有三个任务中表现出色，包括实例分割、边界框物体检测和人体关键点检测。

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Paper: Mask R-CNN

作者: Kaiming He Georgia Gkioxari Piotr Dollar Ross Girshick

摘要：We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks,e.g., allowing us to estimate human poses in the same framework.
We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.

贡献：

基于Faster RCNN的框架，对于ROI增加了instance segmentation分支，利用pixel-to-pixel的mask，来帮助classification和regeression
改进了ROI，提出了ROI Align，对于segmentation的pixel-to-pixel可以对齐的更为准确。

细节：