发表机构
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究大型图像集多目标估计问题，借鉴调查抽样策略，在五个检测和分割数据集上评估，发现重要性采样、带控制变量的均匀采样及基于子集的比率估计器各有优势，有效结合模型预测与人类标签进行科学测量。
AI 中文摘要
计算机视觉模型越来越多地用作测量工具，从大型图像集中估计总体水平的数量，但预测误差会引入偏差，导致估计缺乏科学应用所需的统计保证。先前工作使用蒙特卡罗框架，通过对一些图像采样让人类标注，将模型预测与真实标注结合，能提供具有可控精度的无偏估计，但主要解决单标量估计。本文研究多目标估计这一更普遍问题，即同时估计多个数量（如类别计数或比例），并将调查抽样中的采样和估计策略应用于此场景。在五个有7至80个类别的检测和分割数据集上的评估表明，重要性采样在中等标注预算或较少目标时表现出色，而带控制变量的均匀采样在估计多个目标或使用最少标签时更优。此外，基于子集的比率估计器在所有情况下都具有很强竞争力。最终，我们的框架有效地将有偏差的模型预测和有限的人类标签结合成严格的科学测量。
英文摘要
Computer vision models are increasingly used as measurement tools to estimate population-level quantities from large image collections, but prediction errors introduce bias and the resulting estimates lack statistical guarantees required in scientific applications. Prior work uses a Monte Carlo framework to combine model predictions with ground-truth annotations by sampling some images for humans to label and is able to provide unbiased estimates with controllable accuracy, but primarily addresses single-scalar estimation. We study the more general problem of multi-target estimation, where many quantities (e.g., class counts or proportions) must be estimated simultaneously, and adapt sampling and estimation strategies from survey sampling to this setting. Evaluations on five detection and segmentation datasets with 7-80 classes show that importance sampling excels with moderate annotation budgets or fewer targets, whereas uniform sampling with control variates is superior when estimating many targets or operating with minimal labels. Additionally, a subset-based ratio estimator remains highly competitive across all regimes. Ultimately, our framework effectively combines biased model predictions and limited human labels into rigorous scientific measurements.
CommentsAccepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026)
