Obtaining accurate labels for instance segmentation is particularly challenging due to the complex nature of the task. Each image necessitates multiple annotations, encompassing not only the object's class but also its precise spatial boundaries. These requirements elevate the likelihood of errors and inconsistencies in both manual and automated annotation processes. By simulating different noise conditions, we provide a realistic scenario for assessing the robustness and generalization capabilities of instance segmentation models in different segmentation tasks, introducing COCO-N and Cityscapes-N. We also propose a benchmark for weakly annotation noise, dubbed COCO-WAN, which utilizes foundation models and weak annotations to simulate semi-automated annotation tools and their noisy labels. This study sheds light on the quality of segmentation masks produced by various models and challenges the efficacy of popular methods designed to address learning with label noise.

通过模拟不同噪声条件，对不同分割任务中实例分割模型的鲁棒性和泛化能力进行了评估，引入了COCO-N和Cityscapes-N作为强注释噪声的实验场景。同时，提出了利用基础模型和弱标注来模拟半自动标注工具和其噪声标签的COCO-WAN基准，以研究各种模型生成的分割掩膜的质量，并质疑了常用的处理标签噪声学习的方法的有效性。

实例分割中标签噪声的基准测试：空间噪声的重要性