To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade model, cannot address well the balance issue between accuracy and computation cost. Motivated by the fact that humans distinguish among objects continuously from coarse to precise levels, we propose the Continuous Refinement Model~(CRM) for the ultra high-resolution segmentation refinement task. CRM continuously aligns the feature map with the refinement target and aggregates features to reconstruct these images' details. Besides, our CRM shows its significant generalization ability to fill the resolution gap between low-resolution training images and ultra high-resolution testing ones. We present quantitative performance evaluation and visualization to show that our proposed method is fast and effective on image segmentation refinement. Code will be released at https://github.com/dvlab-research/Entity.

翻译：对于4K或6K超高分辨率图像部分,在图像分割方面需要额外的计算考虑; 普通战略,例如下标、补丁裁剪和级联模型,无法很好地解决精确度和计算成本之间的平衡问题; 人类不断将物体从粗度到精确度区分开来,我们提议为超高分辨率分解完善任务采用连续精炼模型~(CRM) 。 CRM 不断将地貌地图与精炼目标和集成特征相匹配,以重建这些图像的细节。 此外,我们的CRM 显示其相当的通用能力,以填补低分辨率培训图像和超高分辨率测试图像之间的分辨率差距。 我们提出定量绩效评估和可视化,以显示我们拟议方法在图像分割完善方面是快速有效的。 代码将在https://github.com/dvlab-research/Entity发布。