语义分割中的噪声标注
Noisy Annotations in Semantic Segmentation
AI总结：
针对实例分割标签获取困难的问题，通过模拟噪声条件引入COCO-N、Cityscapes-N和COCO-WAN基准，评估模型鲁棒性并检验现有标签噪声学习方法的有效性。
摘要：
由于任务的复杂性，为实例分割获取准确标签尤为困难。每张图像需要多个标注，不仅包括目标类别，还包括其精确的空间边界。这些要求增加了手动和自动标注过程中出现错误和不一致的可能性。通过模拟不同的噪声条件，我们提供了一个真实的场景，用于评估实例分割模型在不同分割任务中的鲁棒性和泛化能力，并引入了 COCO-N 和 Cityscapes-N。我们还提出了一个弱标注噪声基准，名为 COCO-WAN，它利用基础模型和弱标注来模拟半自动标注工具及其噪声标签。本研究揭示了各种模型生成的分割掩码的质量，并挑战了旨在解决标签噪声学习问题的流行方法的有效性。
英文摘要：
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 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.
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