首次系统研究浑浊水下图像分割的标注噪声与不确定性，揭示水质影响与人工差异。
A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
- 招募超100名标注者，控制浊度条件进行多标注实验
- 发现浊度导致系统性标注偏差，标注一致性随能见度下降而降低
- 提出使用专家信息、个体努力和集成标注提升水下标注质量
标签不确定性和标注者分歧是计算机视觉中的常见挑战，但现有研究主要集中在医学领域或通用图像识别数据集。水下图像因需要领域知识、可见度差以及难以建立可靠真实标签，在此方面尤为突出。尽管如此，水下图像的标注不确定性仍鲜有研究。本文首次开展针对真实水下场景分割的系统性多标注研究，参与人数超过100人，覆盖不同可控浊度水平。结果表明，水下数据集面临与其他视觉任务类似的标注挑战，而浊度引入了额外的系统性误差。我们进一步分析了标签噪声的主要驱动因素，并探索了通过特权信息、个体努力和标注员集成等方式改善浑浊水下环境下的标注质量。本研究收集的所有（元）数据将公开于项目页面：https://vap.aau.dk/tubcertainty
原文摘要 · Abstract (English)
Label uncertainty and annotator disagreement are common challenges in the field of computer vision, yet their study has largely been confined to the medical domain or to generic image-recognition datasets. Underwater datasets are particularly susceptible to these issues due to the need for domain expertise, degraded visibility conditions, and the inherent difficulty of establishing reliable ground truth in inaccessible environments. Despite these challenges, annotation uncertainty in underwater imagery remains largely unexplored. In this work, we present the first systematic multi-annotator study of segmentation in real underwater scenes, with over 100 participants, and across varying, controlled levels of turbidity. We show that underwater datasets face many of the same annotation challenges as other vision tasks, while turbidity introduces additional systematic errors. We further investigate the main factors driving label noise and explore ways to improve annotation quality in turbid underwater environments, including privileged information, individual effort and annotator ensembles. All (meta-) data collected in this study will be available on the project page: https://vap.aau.dk/tubcertainty
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。