GaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling
Seung Il Lee, Hyun Kim
Abstract
Instance segmentation, which has been required in various applications in recent years, is aimed at reliable bounding box (bbox) detection (i.e., localization) and stable mask prediction (i.e., segmentation). However, the mask uncertainty problem is still unresolved, which hinders the ability to achieve an accurate instance segmentation. In this paper, we propose GaussianMask, an uncertainty-aware instance segmentation technique based on Gaussian modeling. We can determine the uncertainty of the network through the variance of masks extracted by redesigning the loss function based on Gaussian modeling, and the mask accuracy can be improved by constructing a robust model that adaptively applies this uncertainty to the network. In particular, the additional computations caused during this process are insignificant, and thus negligible for the processing speed of the network. Moreover, GaussianMask has the advantage of being applicable to any network due to its high compatibility. Experimental results show that the mask average precision (AP) of the representative instance segmentation models, YOLACT and Mask R-CNN, increases from 29.7% to 31.8% and from 35.7% to 37.5%, respectively, on the MS-COCO dataset.