GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation

Abstract

Amodal instance segmentation (AIS) predicts the complete shape of the occluded object, including both visible and occluded regions. Because visual clues are lacking, the occluded region is difficult to segment accurately. In human amodal perception, shape-prior knowledge is helpful for AIS. The previous method uses a 2D shape prior by rote memorizing , establishing a shape dictionary and retrieving the closest mask to the segmentation result. However, this approach cannot obtain the shape prior, which is not prestored in the shape dictionary. In this article, to improve generalization ability, we propose a generative invariant shape-prior network (GIN), simulating the human perception process that learns the basic shape, which is invariant to transformations, including translation, rotation, and scaling. We design a novel framework that decouples the learning of shape priors from transformation . GIN is end-to-end trainable and needs no dictionary establishment, making the whole pipeline efficient. GIN outperforms state-of-the-art methods on three public datasets (D2SA, COCOA-cls, and KINS) with large margins.