Abstract
The general method of image instance segmentation is to perform the object
detection first, and then segment the object from the detection bounding-box.
More recently, deep learning methods like Mask R-CNN perform them jointly.
However, little research takes into account the uniqueness of the "1human"
category, which can be well defined by the pose skeleton. In this paper, we
present a brand new pose-based instance segmentation framework for humans which
separates instances based on human pose, not proposal region detection. We
demonstrate that our pose-based framework can achieve similar accuracy to the
detection-based approach, and can moreover better handle occlusion, which is
the most challenging problem in the detection-based framework.
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