Instance segmentation aims to detect and segment individual objects in a
scene. Most existing methods rely on precise mask annotations of every
category. However, it is difficult and costly to segment objects in novel
categories because a large number of mask annotations is required. We introduce
ShapeMask, which learns the intermediate concept of object shape to address the
problem of generalization in instance segmentation to novel categories.
ShapeMask starts with a bounding box detection and gradually refines it by
first estimating the shape of the detected object through a collection of shape
priors. Next, ShapeMask refines the coarse shape into an instance level mask by
learning instance embeddings. The shape priors provide a strong cue for
object-like prediction, and the instance embeddings model the instance specific
appearance information. ShapeMask significantly outperforms the
state-of-the-art by 6.4 and 3.8 AP when learning across categories, and obtains
competitive performance in the fully supervised setting. It is also robust to
inaccurate detections, decreased model capacity, and small training data.
Moreover, it runs efficiently with 150ms inference time and trains within 11
hours on TPUs. With a larger backbone model, ShapeMask increases the gap with
state-of-the-art to 9.4 and 6.2 AP across categories. Code will be released.

介绍了一种名为 ShapeMask 的解决利用对象的形状中间概念来解决在实例分割中应用于新类别的问题的方法。与现有方法相比，该模型通过学习形状先验和实例嵌入来捕获对象形状和外观信息，并取得了 6.4 到 9.4 的 AP 值提高。