用有限人工标注校准模型，实现大图像集多目标精准统计。
Scalable Model-Assisted Multi-Target Estimation in Large Image Collections
- 引入调查抽样思想，设计多目标估计的采样策略
- 7-80类场景下，重要性采样在中等预算时更优
- 适合需要科学严谨性的生物、医学图像分析
计算机视觉模型正被用于从大规模图像集合中估算群体水平指标，但预测误差会引入偏差，导致结果缺乏科学应用所需的统计保证。已有工作采用蒙特卡洛框架，通过采样部分图像进行人工标注来结合模型预测与真实标签，可实现无偏估计且精度可控，但主要解决单标量估计问题。本文研究更通用的多目标估计任务，即同时估计多个量（如各类别数量或比例），并将其抽样与估计策略从调查抽样领域适配到该场景。在包含7至80个类别的5个检测与分割数据集上的评估表明：在中等标注预算或目标数较少时，重要性采样表现最佳；而在需估计大量目标或标注极有限时，均匀采样结合控制变量法更优。此外，基于子集的比率估计器在所有情形下均保持高竞争力。最终，本框架能有效融合有偏模型预测与有限人工标注，生成可靠的科学测量结果。
原文摘要 · Abstract (English)
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.
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