We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce connectivity of each region, with a notable class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorithms can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We then propose an interactive, scribble-based annotation framework.

翻译：我们处理的是交互式全光说明,即一个部分的图像中所有物体和东西区域。我们调查了两种基于图形的分解算法,这两种算法都强制每个区域连接,并配有一种能确保全球最佳最佳的有等级认知的整线性编程(ILP)配方。 两种算法都可以使用RGB,或者使用来自任何DCNN的地貌地图作为输入,不管是否受过目标数据集的培训。然后我们提出一个互动的、基于拼字的批注框架。