Abstract
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.
Abstract (translated)
获得准确的分割标签尤其具有挑战性,因为任务的复杂性。每个图像都需要多个注释,不仅包括对象的类,还包括其精确的空间边界。这些要求使得在手动和自动注释过程中出现错误和不一致的可能性大大增加。通过模拟不同的噪声情况,我们提供了一个评估不同分割任务中实例分割模型鲁棒性和泛化能力的现实场景,引入了COCO-N和Cityscapes-N。我们还提出了一个基准,称为COCO-WAN,用于弱标注噪音,它利用基础模型和弱注释来模拟半自动注释工具和它们的噪音标签。这项研究揭示了各种模型产生的分割掩模的质量,并挑战了旨在解决带标签噪声学习的常见方法的实效性。
URL
https://arxiv.org/abs/2406.10891
