We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as “ambiguous” or “not ambiguous” to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid “ground truth” foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47% of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths.

中文翻译：

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预测前景对象歧义并有效地众包分割

我们提出了前景对象分割任务的歧义问题，并激发了在设计视觉系统时估计和解释这种歧义的重要性。具体来说，我们区分了导致多个注释器分割不同前景对象（模糊）的图像与同一对象的微小注释器间差异。从八个广泛使用的数据集中获取图像，我们将图像众包标记为“模棱两可”或“不模棱两可”进行分割，以构建我们称为 STATIC 的新数据集。使用 STATIC，我们开发了一个系统，可以自动预测哪些图像不明确。实验证明了我们的预测系统优于现有的基于显着性的方法，这些方法来自视觉基准的图像和试图识别环境中物体的盲人拍摄的图像。最后，我们引入了一个众包系统，以通过仅在预期歧义时收集额外的分割来节省收集所有有效“地面实况”前景对象分割的多样性的成本。实验表明，与现有的众包方法相比，我们的系统减少了高达 47% 的人力，并且不会损失捕获基本事实的多样性。