论文标题
跨形状和外观的公用性网络，用于部分监督实例细分
Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation
论文作者
论文摘要
部分监督的实例细分旨在对有限的掩护数据类别进行学习，从而消除昂贵且详尽的面具注释。学到的模型预计可以推广到新颖的类别。现有方法可以学习从检测到分割的传输函数，或者用于分割新类别的群集形状先验。我们建议学习可以从面具注销类别推广到新颖类的基本类不足的共同点。具体而言，我们解析了两种类型的共同点：1）以边界预测进行监督学习来学习的形状共同点； 2）外观共同点是通过对特征图像素之间的成对亲和力进行建模以优化实例和背景之间的可分离性来捕获的。我们的模型同时结合了形状和外观共同点，在部分监督的设置和COCO数据集上的细分设置中都大大优于最先进的方法。
Partially supervised instance segmentation aims to perform learning on limited mask-annotated categories of data thus eliminating expensive and exhaustive mask annotation. The learned models are expected to be generalizable to novel categories. Existing methods either learn a transfer function from detection to segmentation, or cluster shape priors for segmenting novel categories. We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Specifically, we parse two types of commonalities: 1) shape commonalities which are learned by performing supervised learning on instance boundary prediction; and 2) appearance commonalities which are captured by modeling pairwise affinities among pixels of feature maps to optimize the separability between instance and the background. Incorporating both the shape and appearance commonalities, our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset.
