Toward Real Ultra Image Segmentation: Leveraging Surrounding Context to Cultivate General Segmentation Model

Existing ultra image segmentation methods suffer from two major challenges, namely the scalability issue (i.e. they lack the stability and generality of standard segmentation models, as they are tailored to specific datasets), and the architectural issue (i.e. they are incompatible with real-world ultra image scenes, as they compromise between image size and computing resources). To tackle these issues, we revisit the classic sliding inference framework, upon which we propose a Sur-rounding Guided Segmentation framework (SGNet) for ultra image segmentation. The SGNet leverages a larger area around each image patch to refine the general segmentation results of local patches. Specifically, we propose a surrounding context integration module to absorb surrounding context information and extract specific features that are beneficial to local patches. Note that, SGNet can be seamlessly integrated to any general segmentation model. Extensive experiments on five datasets demonstrate that SGNet achieves competitive performance and consistent improvements across a variety of general segmentation models, surpassing the traditional ultra image segmentation methods by a large margin.