This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches. Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instance-aware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.

该研究采用新的建模方法（InstanceCut），通过卷积神经网络和新的实例感知边缘检测模型实现两种输出模态，即无关实例的语义分割和所有实例边界。进一步地，将这两种模态融合为一种新的MultiCut formulation，并在CityScapes数据集上获得了比已有方法更好的结果。

InstanceCut: 从边缘到实例的多切分