审核长尾人类数据策展中的质量过滤器
Auditing Quality Filters for Long-Tail Human Data Curation
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中文总结 AI 辅助
本研究针对建筑工地低姿态工人检测数据稀缺问题，通过四种姿态线索及模拟实验揭示质量过滤器对低姿态的过度拒绝，表明无法依赖边界框或姿态进行长尾数据策展。
中文摘要 AI 辅助
建筑工地上的机器人必须检测出跪着或弯腰的工人，我们称之为低姿态。这些工人在自动标注过程中可能会从训练数据集中丢失。我们研究了一个检测人员、使用NLF估计身体关节并分组相似姿态的流程。低姿态仅占保留示例的约2%。这一低比例可能部分反映了该流程的质量过滤器，该过滤器会拒绝检测置信度低或关节估计不确定的示例。我们使用四种替代姿态线索来检查这种过滤：边界框形状、垂直身体跨度、与场景垂直方向对齐的姿态分组以及图像外观。所有四种线索都表明低姿态被过滤器更频繁地拒绝。此外，受控模拟场景显示，在公共建筑数据集上微调的人员检测器即使我们将其边界框高度校正为与站立工人相同，也会遗漏更多处于这些姿态的工人。这些发现表明低姿态是稀缺且难以发现的，我们不能依赖边界框或姿态来进行长尾人类数据策展。
英文摘要
Robots on construction sites must detect workers who are kneeling or bending, which we call low poses. These workers can be lost from training datasets during automatic labeling. We study a pipeline that detects people, estimates their body joints using NLF, and groups similar poses. Low poses account for only about 2 percent of the retained examples. This low share may partly reflect the pipeline's quality filter, which rejects examples with low detection confidence or uncertain joint estimates. We examine this filtering using four alternative pose clues: bounding-box shape, vertical body span, pose grouping aligned to the scene's vertical direction, and image appearance. All four suggest that low poses are rejected by the filter more often. Separately, controlled simulated scenes show that a person detector fine-tuned on a public construction dataset misses more workers in these poses even when we correct their bounding box height is matched to that of standing workers. These findings suggest that low poses are scarce and hard to find, and we cannot rely on bounding boxes or poses for long-tail human data curation.
